{"task":"Graph Classification","dataset":"PROTEINS","metric_names":["Accuracy","Accuracy (10 fold)","Inference Time (ms)"],"rows":[{"id":95556,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"HGP-SL","metrics":{"Accuracy":"84.91"},"paper_url":"https://arxiv.org/abs/1911.05954v3","paper_title":"Hierarchical Graph Pooling with Structure Learning","paper_date":"2019-11-14","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/hgp_sl"},{"title":"cszhangzhen/HGP-SL","url":"https://github.com/cszhangzhen/HGP-SL"},{"title":"gsl-benchmark/gslb","url":"https://github.com/gsl-benchmark/gslb"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95557,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"rLap (unsupervised)","metrics":{"Accuracy":"84.3"},"paper_url":"https://arxiv.org/abs/2306.04004v1","paper_title":"Randomized Schur Complement Views for Graph Contrastive Learning","paper_date":"2023-06-06","code_links":[{"title":"kvignesh1420/rlap","url":"https://github.com/kvignesh1420/rlap"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95558,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"TFGW ADJ (L=2)","metrics":{"Accuracy":"82.9"},"paper_url":"https://arxiv.org/abs/2205.15733v1","paper_title":"Template based Graph Neural Network with Optimal Transport Distances","paper_date":"2022-05-31","code_links":[{"title":"cedricvincentcuaz/TFGW","url":"https://github.com/cedricvincentcuaz/TFGW"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95559,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"ESA (Edge set attention, no positional encodings)","metrics":{"Accuracy":"82.679±0.799"},"paper_url":"https://arxiv.org/abs/2402.10793v2","paper_title":"An end-to-end attention-based approach for learning on graphs","paper_date":"2024-02-16","code_links":[{"title":"davidbuterez/edge-set-attention","url":"https://github.com/davidbuterez/edge-set-attention"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95560,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"FIT-GNN","metrics":{"Accuracy":"82.1","Inference Time (ms)":"0.0016"},"paper_url":"https://arxiv.org/abs/2410.15001v2","paper_title":"FIT-GNN: Faster Inference Time for GNNs Using Coarsening","paper_date":"2024-10-19","code_links":[{"title":"Roy-Shubhajit/FIT-GNN","url":"https://github.com/Roy-Shubhajit/FIT-GNN"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95561,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DUGNN","metrics":{"Accuracy":"81.70%"},"paper_url":"https://arxiv.org/abs/1909.10086v3","paper_title":"Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning","paper_date":"2019-09-22","code_links":[{"title":"vermaMachineLearning/Universal-Graph-Embedding-Neural-Network","url":"https://github.com/vermaMachineLearning/Universal-Graph-Embedding-Neural-Network"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":1,"source":"archive","tags":[]},{"id":95562,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"MEWISPool","metrics":{"Accuracy":"80.71%"},"paper_url":"https://arxiv.org/abs/2107.01410v1","paper_title":"Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks","paper_date":"2021-07-03","code_links":[{"title":"mewispool/mewispool","url":"https://github.com/mewispool/mewispool"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95563,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"CIN++","metrics":{"Accuracy":"80.5"},"paper_url":"https://arxiv.org/abs/2306.03561v1","paper_title":"CIN++: Enhancing Topological Message Passing","paper_date":"2023-06-06","code_links":[{"title":"twitter-research/cwn","url":"https://github.com/twitter-research/cwn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95564,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"SAEPool","metrics":{"Accuracy":"80.36%"},"paper_url":"https://ieeexplore.ieee.org/abstract/document/9750933","paper_title":"Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization","paper_date":"2022-04-06","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95565,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"MAGPool","metrics":{"Accuracy":"80.36%"},"paper_url":"https://ieeexplore.ieee.org/document/10454077","paper_title":"Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective","paper_date":"2024-03-04","code_links":[{"title":"p-haddadian/MAGPool","url":"https://github.com/p-haddadian/MAGPool"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95566,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"UGT","metrics":{"Accuracy":"80.12 ±0.32"},"paper_url":"https://arxiv.org/abs/2308.09517v1","paper_title":"Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity","paper_date":"2023-08-18","code_links":[{"title":"nslab-cuk/unified-graph-transformer","url":"https://github.com/nslab-cuk/unified-graph-transformer"},{"title":"nslab-cuk/community-aware-graph-transformer","url":"https://github.com/nslab-cuk/community-aware-graph-transformer"},{"title":"nslab-cuk/literalkg","url":"https://github.com/nslab-cuk/literalkg"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95567,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"U2GNN (Unsupervised)","metrics":{"Accuracy":"80.01%"},"paper_url":"https://arxiv.org/abs/1909.11855v9","paper_title":"Universal Graph Transformer Self-Attention Networks","paper_date":"2019-09-26","code_links":[{"title":"daiquocnguyen/Graph-Transformer","url":"https://github.com/daiquocnguyen/Graph-Transformer"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95568,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"sGIN","metrics":{"Accuracy":"78.97%"},"paper_url":"https://arxiv.org/abs/1905.08509v4","paper_title":"Mutual Information Maximization in Graph Neural Networks","paper_date":"2019-05-21","code_links":[{"title":"CODE-SUBMIT/Graph_Neighborhood_1","url":"https://github.com/CODE-SUBMIT/Graph_Neighborhood_1"},{"title":"CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network","url":"https://github.com/CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95569,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"QS-CNNs (Quantum Walk)","metrics":{"Accuracy":"78.80%"},"paper_url":"https://doi.org/10.1016/j.patcog.2018.11.002","paper_title":"Quantum-based subgraph convolutional neural networks","paper_date":"2019-04-01","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95570,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"WKPI-kmeans","metrics":{"Accuracy":"78.8%"},"paper_url":"https://arxiv.org/abs/1904.12189v2","paper_title":"Learning metrics for persistence-based summaries and applications for graph classification","paper_date":"2019-04-27","code_links":[{"title":"topology474/WKPI","url":"https://github.com/topology474/WKPI"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95571,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"PIN","metrics":{"Accuracy":"78.8%"},"paper_url":"https://arxiv.org/abs/2308.06838v6","paper_title":"Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes","paper_date":"2023-08-13","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95572,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"hGANet","metrics":{"Accuracy":"78.65%"},"paper_url":"https://arxiv.org/abs/1907.04652v1","paper_title":"Graph Representation Learning via Hard and Channel-Wise Attention Networks","paper_date":"2019-07-05","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/hardgat"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95573,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"U2GNN","metrics":{"Accuracy":"78.53%"},"paper_url":"https://arxiv.org/abs/1909.11855v9","paper_title":"Universal Graph Transformer Self-Attention Networks","paper_date":"2019-09-26","code_links":[{"title":"daiquocnguyen/Graph-Transformer","url":"https://github.com/daiquocnguyen/Graph-Transformer"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95574,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DS-CNNs (Random Walk)","metrics":{"Accuracy":"78.35%"},"paper_url":"https://doi.org/10.1016/j.patcog.2018.11.002","paper_title":"Quantum-based subgraph convolutional neural networks","paper_date":"2019-04-01","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95575,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"cGANet","metrics":{"Accuracy":"78.23%"},"paper_url":"https://arxiv.org/abs/1907.04652v1","paper_title":"Graph Representation Learning via Hard and Channel-Wise Attention Networks","paper_date":"2019-07-05","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/hardgat"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95576,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"CAN","metrics":{"Accuracy":"78.2%"},"paper_url":"https://arxiv.org/abs/2209.08179v1","paper_title":"Cell Attention Networks","paper_date":"2022-09-16","code_links":[{"title":"lrnzgiusti/can","url":"https://github.com/lrnzgiusti/can"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95577,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GANet","metrics":{"Accuracy":"77.92%"},"paper_url":"https://arxiv.org/abs/1907.04652v1","paper_title":"Graph Representation Learning via Hard and Channel-Wise Attention Networks","paper_date":"2019-07-05","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/hardgat"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95578,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GraphStar","metrics":{"Accuracy":"77.90%"},"paper_url":"https://arxiv.org/abs/1906.12330v1","paper_title":"Graph Star Net for Generalized Multi-Task Learning","paper_date":"2019-06-21","code_links":[{"title":"graph-star-team/graph_star","url":"https://github.com/graph-star-team/graph_star"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95579,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"NERO","metrics":{"Accuracy":"77.89%"},"paper_url":"https://link.springer.com/chapter/10.1007/978-3-030-77964-1_18","paper_title":"Relation order histograms as a network embedding tool","paper_date":"2021-06-09","code_links":[{"title":"Prpht/NERO","url":"https://github.com/Prpht/NERO"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95580,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Fea2Fea-s2","metrics":{"Accuracy":"77.8%"},"paper_url":"https://arxiv.org/abs/2106.13061v4","paper_title":"Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks","paper_date":"2021-06-24","code_links":[{"title":"JIAQING-XIE/Fea2Fea","url":"https://github.com/JIAQING-XIE/Fea2Fea"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95581,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DGA","metrics":{"Accuracy":"77.71%"},"paper_url":"https://doi.org/10.1109/ICBK.2018.00033","paper_title":"Discriminative Graph Autoencoder","paper_date":"2018-11-17","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95582,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Graph U-Nets","metrics":{"Accuracy":"77.68%"},"paper_url":"https://arxiv.org/abs/1905.05178v1","paper_title":"Graph U-Nets","paper_date":"2019-05-11","code_links":[{"title":"HongyangGao/gunet","url":"https://github.com/HongyangGao/gunet"},{"title":"HongyangGao/Graph-U-Nets","url":"https://github.com/HongyangGao/Graph-U-Nets"},{"title":"hujilin1229/gUNet","url":"https://github.com/hujilin1229/gUNet"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95583,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GATv2","metrics":{"Accuracy":"77.679±2.187"},"paper_url":"https://arxiv.org/abs/2105.14491v3","paper_title":"How Attentive are Graph Attention Networks?","paper_date":"2021-05-30","code_links":[{"title":"labmlai/annotated_deep_learning_paper_implementations","url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations"},{"title":"rusty1s/pytorch_geometric","url":"https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/nn/conv/gatv2_conv.py"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/blob/master/python/dgl/nn/pytorch/conv/gatv2conv.py"},{"title":"tensorflow/gnn","url":"https://github.com/tensorflow/gnn/blob/main/tensorflow_gnn/docs/api_docs/python/gnn/keras/layers/GATv2.md"},{"title":"tech-srl/how_attentive_are_gats","url":"https://github.com/tech-srl/how_attentive_are_gats"},{"title":"machadoprx/GATv2-TensorFlow2","url":"https://github.com/machadoprx/GATv2-TensorFlow2"},{"title":"mahequn001/graphlearning","url":"https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gatv2"},{"title":"briangodwinlim/sir-gcn","url":"https://github.com/briangodwinlim/sir-gcn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95584,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"PNA","metrics":{"Accuracy":"77.679±3.281"},"paper_url":"https://arxiv.org/abs/2004.05718v5","paper_title":"Principal Neighbourhood Aggregation for Graph Nets","paper_date":"2020-04-12","code_links":[{"title":"rusty1s/pytorch_geometric","url":"https://github.com/rusty1s/pytorch_geometric"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl"},{"title":"lukecavabarrett/pna","url":"https://github.com/lukecavabarrett/pna"},{"title":"Saro00/DGN","url":"https://github.com/Saro00/DGN"},{"title":"alexOarga/haiku-geometric","url":"https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/pna_conv.py"},{"title":"cvignac/SMP","url":"https://github.com/cvignac/SMP"},{"title":"asarigun/GraphMixerNetworks","url":"https://github.com/asarigun/GraphMixerNetworks"},{"title":"changminwu/expandergnn","url":"https://github.com/changminwu/expandergnn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95585,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GIC","metrics":{"Accuracy":"77.65%"},"paper_url":"http://arxiv.org/abs/1811.04393v1","paper_title":"Gaussian-Induced Convolution for Graphs","paper_date":"2018-11-11","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95586,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GIUNet","metrics":{"Accuracy":"77.6%"},"paper_url":"https://www.sciencedirect.com/science/article/abs/pii/S0957417423017827#preview-section-abstract","paper_title":"Graph isomorphism UNet","paper_date":"2023-08-23","code_links":[{"title":"aramiracle/GIUNet","url":"https://github.com/aramiracle/GIUNet"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95587,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GFN-light","metrics":{"Accuracy":"77.44%"},"paper_url":"https://arxiv.org/abs/1905.04579v3","paper_title":"Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification","paper_date":"2019-05-11","code_links":[{"title":"chentingpc/gfn","url":"https://github.com/chentingpc/gfn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95588,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"SEAL-SAGE","metrics":{"Accuracy":"77.26%"},"paper_url":"http://arxiv.org/abs/1904.05003v1","paper_title":"Semi-Supervised Graph Classification: A Hierarchical Graph Perspective","paper_date":"2019-04-10","code_links":[{"title":"benedekrozemberczki/SEAL-CI","url":"https://github.com/benedekrozemberczki/SEAL-CI"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95589,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"PPGN","metrics":{"Accuracy":"77.20%"},"paper_url":"https://arxiv.org/abs/1905.11136v4","paper_title":"Provably Powerful Graph Networks","paper_date":"2019-05-27","code_links":[{"title":"hadarser/ProvablyPowerfulGraphNetworks_torch","url":"https://github.com/hadarser/ProvablyPowerfulGraphNetworks_torch"},{"title":"hadarser/ProvablyPowerfulGraphNetworks","url":"https://github.com/hadarser/ProvablyPowerfulGraphNetworks"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95590,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GraphGPS","metrics":{"Accuracy":"77.143±1.494"},"paper_url":"https://arxiv.org/abs/2205.12454v4","paper_title":"Recipe for a General, Powerful, Scalable Graph Transformer","paper_date":"2022-05-25","code_links":[{"title":"rampasek/GraphGPS","url":"https://github.com/rampasek/GraphGPS"},{"title":"hamed1375/exphormer","url":"https://github.com/hamed1375/exphormer"},{"title":"graphcore/ogb-lsc-pcqm4mv2","url":"https://github.com/graphcore/ogb-lsc-pcqm4mv2"},{"title":"linusbao/MoSE","url":"https://github.com/linusbao/MoSE"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95591,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Weak-supervised ChebyNet","metrics":{"Accuracy":"77.09%"},"paper_url":"https://arxiv.org/abs/1905.02850v3","paper_title":"Understanding Attention and Generalization in Graph Neural Networks","paper_date":"2019-05-08","code_links":[{"title":"bknyaz/graph_nn","url":"https://github.com/bknyaz/graph_nn"},{"title":"bknyaz/graph_attention_pool","url":"https://github.com/bknyaz/graph_attention_pool"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95592,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"SEG-BERT","metrics":{"Accuracy":"77.09%"},"paper_url":"https://arxiv.org/abs/2002.03283v1","paper_title":"Segmented Graph-Bert for Graph Instance Modeling","paper_date":"2020-02-09","code_links":[{"title":"jwzhanggy/SEG-BERT","url":"https://github.com/jwzhanggy/SEG-BERT"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95593,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GAT-GC (f-Scaled)","metrics":{"Accuracy":"76.81%"},"paper_url":"https://arxiv.org/abs/1907.02204v4","paper_title":"Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation","paper_date":"2019-07-04","code_links":[{"title":"zetayue/CPA","url":"https://github.com/zetayue/CPA"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95594,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GAT","metrics":{"Accuracy":"76.786±1.670"},"paper_url":"http://arxiv.org/abs/1710.10903v3","paper_title":"Graph Attention Networks","paper_date":"2017-10-30","code_links":[{"title":"labmlai/annotated_deep_learning_paper_implementations","url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat"},{"title":"PetarV-/GAT","url":"https://github.com/PetarV-/GAT"},{"title":"Diego999/pyGAT","url":"https://github.com/Diego999/pyGAT"},{"title":"gordicaleksa/pytorch-GAT","url":"https://github.com/gordicaleksa/pytorch-GAT"},{"title":"shenweichen/GraphNeuralNetwork","url":"https://github.com/shenweichen/GraphNeuralNetwork"},{"title":"HazyResearch/hgcn","url":"https://github.com/HazyResearch/hgcn"},{"title":"GraphSAINT/GraphSAINT","url":"https://github.com/GraphSAINT/GraphSAINT"},{"title":"danielegrattarola/keras-gat","url":"https://github.com/danielegrattarola/keras-gat"},{"title":"lukecavabarrett/pna","url":"https://github.com/lukecavabarrett/pna"},{"title":"meliketoy/graph-cnn.pytorch","url":"https://github.com/meliketoy/graph-cnn.pytorch"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/gnn/gat"},{"title":"Kaimaoge/IGNNK","url":"https://github.com/Kaimaoge/IGNNK"},{"title":"IllinoisGraphBenchmark/IGB-Datasets","url":"https://github.com/IllinoisGraphBenchmark/IGB-Datasets"},{"title":"arangoml/fastgraphml","url":"https://github.com/arangoml/fastgraphml"},{"title":"zhao-tong/GNNs-easy-to-use","url":"https://github.com/zhao-tong/GNNs-easy-to-use"},{"title":"davidpicard/homm","url":"https://github.com/davidpicard/homm"},{"title":"alexOarga/haiku-geometric","url":"https://github.com/alexOarga/haiku-geometric"},{"title":"snowkylin/gnn","url":"https://github.com/snowkylin/gnn"},{"title":"akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network","url":"https://github.com/akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network"},{"title":"fanzhenliu/dagad","url":"https://github.com/fanzhenliu/dagad"},{"title":"weiyangfb/PyTorchSparseGAT","url":"https://github.com/weiyangfb/PyTorchSparseGAT"},{"title":"HeapHop30/graph-attention-nets","url":"https://github.com/HeapHop30/graph-attention-nets"},{"title":"marblet/GNN_models_pytorch_geometric","url":"https://github.com/marblet/GNN_models_pytorch_geometric"},{"title":"marble0117/GNN_models_pytorch_geometric","url":"https://github.com/marble0117/GNN_models_pytorch_geometric"},{"title":"marble0117/GNN_models_pytorch","url":"https://github.com/marble0117/GNN_models_pytorch"},{"title":"zxhhh97/ABot","url":"https://github.com/zxhhh97/ABot"},{"title":"ds4dm/sGat","url":"https://github.com/ds4dm/sGat"},{"title":"ds4dm/sparse-gcn","url":"https://github.com/ds4dm/sparse-gcn"},{"title":"isotlaboratory/ml4vrp","url":"https://github.com/isotlaboratory/ml4vrp"},{"title":"marblet/gat-pytorch","url":"https://github.com/marblet/gat-pytorch"},{"title":"ydzhang-stormstout/HAT","url":"https://github.com/ydzhang-stormstout/HAT"},{"title":"gcucurull/jax-gat","url":"https://github.com/gcucurull/jax-gat"},{"title":"calciver/Graph-Attention-Networks","url":"https://github.com/calciver/Graph-Attention-Networks"},{"title":"shi27feng/transformers.satisfy","url":"https://github.com/shi27feng/transformers.satisfy"},{"title":"pengsl-lab/mssl","url":"https://github.com/pengsl-lab/mssl"},{"title":"pengsl-lab/mssl2drug","url":"https://github.com/pengsl-lab/mssl2drug"},{"title":"nnaakkaaii/g2-MLP","url":"https://github.com/nnaakkaaii/g2-MLP"},{"title":"gmum/umwpl2021","url":"https://github.com/gmum/umwpl2021"},{"title":"taishan1994/pytorch_gat","url":"https://github.com/taishan1994/pytorch_gat"},{"title":"wan2000/mc-mot","url":"https://github.com/wan2000/mc-mot"},{"title":"basiralab/RG-Select","url":"https://github.com/basiralab/RG-Select"},{"title":"dmeoli/neuro-sat","url":"https://github.com/dmeoli/neuro-sat"},{"title":"liu6zijian/simplified-gcn-model","url":"https://github.com/liu6zijian/simplified-gcn-model"},{"title":"tatp22/pytorch-fast-GAT","url":"https://github.com/tatp22/pytorch-fast-GAT"},{"title":"noahtren/Graph-Attention-Networks-TensorFlow-2","url":"https://github.com/noahtren/Graph-Attention-Networks-TensorFlow-2"},{"title":"yururyuru00/GAT_pyG","url":"https://github.com/yururyuru00/GAT_pyG"},{"title":"qema/orca-py","url":"https://github.com/qema/orca-py"},{"title":"zhanglab-aim/cancer-net","url":"https://github.com/zhanglab-aim/cancer-net"},{"title":"gayanku/FDGATII","url":"https://github.com/gayanku/FDGATII"},{"title":"Yindong-Zhang/myGAT","url":"https://github.com/Yindong-Zhang/myGAT"},{"title":"giuseppefutia/link-prediction-code","url":"https://github.com/giuseppefutia/link-prediction-code"},{"title":"Aveek-Saha/Graph-Attention-Net","url":"https://github.com/Aveek-Saha/Graph-Attention-Net"},{"title":"BIG-S2/keras-gnm","url":"https://github.com/BIG-S2/keras-gnm"},{"title":"mlzxzhou/keras-gnm","url":"https://github.com/mlzxzhou/keras-gnm"},{"title":"basiralab/Reproducible-Generative-Learning","url":"https://github.com/basiralab/Reproducible-Generative-Learning"},{"title":"abhilash1910/GraphAttentionNetworks","url":"https://github.com/abhilash1910/GraphAttentionNetworks"},{"title":"Kristof-Neys/GAT_Neo4j_DGL","url":"https://github.com/Kristof-Neys/GAT_Neo4j_DGL"},{"title":"ak24watch/Graph-Deeplearning-GAT","url":"https://github.com/ak24watch/Graph-Deeplearning-GAT"},{"title":"Negus25/GAT-Mindspore","url":"https://github.com/Negus25/GAT-Mindspore"},{"title":"ChengyuSun/gat","url":"https://github.com/ChengyuSun/gat"},{"title":"joaopedromattos/gnee","url":"https://github.com/joaopedromattos/gnee"},{"title":"mitya8128/experiments_notes","url":"https://github.com/mitya8128/experiments_notes"},{"title":"4pygmalion/GAT_tensorflow","url":"https://github.com/4pygmalion/GAT_tensorflow"},{"title":"vailatuts/SSFG-regularization","url":"https://github.com/vailatuts/SSFG-regularization"},{"title":"handasontam/GAT-with-edgewise-attention","url":"https://github.com/handasontam/GAT-with-edgewise-attention"},{"title":"Mind23-2/MindCode-4","url":"https://github.com/Mind23-2/MindCode-4/tree/main/gat"},{"title":"AngusMonroe/GAT-pytorch","url":"https://github.com/AngusMonroe/GAT-pytorch"},{"title":"fongyk/graph-attention","url":"https://github.com/fongyk/graph-attention"},{"title":"404nofound/gat","url":"https://github.com/404nofound/gat"},{"title":"ColdenChan/GAT_D","url":"https://github.com/ColdenChan/GAT_D"},{"title":"MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints","url":"https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints"},{"title":"bowu/GraphSearch","url":"https://github.com/bowu/GraphSearch"},{"title":"Messham87/COVID_GAT","url":"https://github.com/Messham87/COVID_GAT"},{"title":"zhangbo2008/GAT_network","url":"https://github.com/zhangbo2008/GAT_network"},{"title":"blueberryc/pyGAT","url":"https://github.com/blueberryc/pyGAT"},{"title":"anish-lu-yihe/SVRT-by-GAT","url":"https://github.com/anish-lu-yihe/SVRT-by-GAT"},{"title":"WantingZhao/my_GAT","url":"https://github.com/WantingZhao/my_GAT"},{"title":"dzb1998/pyGAT","url":"https://github.com/dzb1998/pyGAT"},{"title":"subercui/pyGConvAT","url":"https://github.com/subercui/pyGConvAT"},{"title":"YunseobShin/wiki_GAT","url":"https://github.com/YunseobShin/wiki_GAT"},{"title":"MindSpore-scientific-2/code-4","url":"https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU"},{"title":"snownus/COOP","url":"https://github.com/snownus/COOP"},{"title":"ChengSashankh/trying-GAT","url":"https://github.com/ChengSashankh/trying-GAT"},{"title":"Anak2016/GAT","url":"https://github.com/Anak2016/GAT"},{"title":"whut2962575697/gat_sementic_segmentation","url":"https://github.com/whut2962575697/gat_sementic_segmentation"},{"title":"Anou9531/GAT","url":"https://github.com/Anou9531/GAT"},{"title":"PumpkinYing/GAT","url":"https://github.com/PumpkinYing/GAT"},{"title":"galkampel/HyperNetworks","url":"https://github.com/galkampel/HyperNetworks"},{"title":"tlmakinen/GAT-noise","url":"https://github.com/tlmakinen/GAT-noise"},{"title":"TyngJiunKuo/deep-learning-project","url":"https://github.com/TyngJiunKuo/deep-learning-project"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95595,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Deep WL SGN(0,1,2)","metrics":{"Accuracy":"76.78%"},"paper_url":"https://arxiv.org/abs/1903.09022v3","paper_title":"Subgraph Networks with Application to Structural Feature Space Expansion","paper_date":"2019-03-21","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95596,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"EigenGCN-3","metrics":{"Accuracy":"76.60%"},"paper_url":"https://arxiv.org/abs/1904.13107v2","paper_title":"Graph Convolutional Networks with EigenPooling","paper_date":"2019-04-30","code_links":[{"title":"alge24/eigenpooling","url":"https://github.com/alge24/eigenpooling"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95597,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Multigraph ChebNet","metrics":{"Accuracy":"76.5%"},"paper_url":"http://arxiv.org/abs/1811.09595v1","paper_title":"Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules","paper_date":"2018-11-23","code_links":[{"title":"bknyaz/graph_nn","url":"https://github.com/bknyaz/graph_nn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95598,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"WEGL","metrics":{"Accuracy":"76.5%"},"paper_url":"https://arxiv.org/abs/2006.09430v2","paper_title":"Wasserstein Embedding for Graph Learning","paper_date":"2020-06-16","code_links":[{"title":"navid-naderi/WEGL","url":"https://github.com/navid-naderi/WEGL"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95599,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"2-WL-GNN","metrics":{"Accuracy":"76.5"},"paper_url":"https://arxiv.org/abs/2007.00346v2","paper_title":"A Novel Higher-order Weisfeiler-Lehman Graph Convolution","paper_date":"2020-07-01","code_links":[{"title":"Cortys/master-thesis","url":"https://github.com/Cortys/master-thesis"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95600,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GFN","metrics":{"Accuracy":"76.46%"},"paper_url":"https://arxiv.org/abs/1905.04579v3","paper_title":"Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification","paper_date":"2019-05-11","code_links":[{"title":"chentingpc/gfn","url":"https://github.com/chentingpc/gfn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95601,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Shortest-Path Kernel","metrics":{"Accuracy":"76.4%"},"paper_url":"https://arxiv.org/abs/1810.02244v5","paper_title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","paper_date":"2018-10-04","code_links":[{"title":"chrsmrrs/k-gnn","url":"https://github.com/chrsmrrs/k-gnn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95602,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GCAPS-CNN","metrics":{"Accuracy":"76.40%"},"paper_url":"http://arxiv.org/abs/1805.08090v4","paper_title":"Graph Capsule Convolutional Neural Networks","paper_date":"2018-05-21","code_links":[{"title":"vermaMachineLearning/Graph-Capsule-CNN-Networks","url":"https://github.com/vermaMachineLearning/Graph-Capsule-CNN-Networks"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95603,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"WL-OA","metrics":{"Accuracy":"76.4%"},"paper_url":"http://arxiv.org/abs/1606.01141v3","paper_title":"On Valid Optimal Assignment Kernels and Applications to Graph Classification","paper_date":"2016-06-03","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95604,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"MLG","metrics":{"Accuracy":"76.34%"},"paper_url":"http://arxiv.org/abs/1603.06186v2","paper_title":"The Multiscale Laplacian Graph Kernel","paper_date":"2016-03-20","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95605,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DAGCN","metrics":{"Accuracy":"76.33%"},"paper_url":"http://arxiv.org/abs/1904.02278v1","paper_title":"DAGCN: Dual Attention Graph Convolutional Networks","paper_date":"2019-04-04","code_links":[{"title":"dawenzi123/DAGCN","url":"https://github.com/dawenzi123/DAGCN"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95606,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"CORE-SP","metrics":{"Accuracy":"76.31%"},"paper_url":"https://arxiv.org/abs/1904.12218v2","paper_title":"Graph Kernels: A Survey","paper_date":"2019-04-27","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95607,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DropGIN","metrics":{"Accuracy":"76.3%"},"paper_url":"https://arxiv.org/abs/2111.06283v1","paper_title":"DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks","paper_date":"2021-11-11","code_links":[{"title":"karolismart/dropgnn","url":"https://github.com/karolismart/dropgnn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95608,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"CapsGNN","metrics":{"Accuracy":"76.28%"},"paper_url":"https://openreview.net/forum?id=Byl8BnRcYm","paper_title":"Capsule Graph Neural Network","paper_date":"2019-05-01","code_links":[{"title":"benedekrozemberczki/CapsGNN","url":"https://github.com/benedekrozemberczki/CapsGNN"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95609,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DGCNN","metrics":{"Accuracy":"76.26%"},"paper_url":"https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17146","paper_title":"An End-to-End Deep Learning Architecture for Graph Classification","paper_date":"2018-04-29","code_links":[{"title":"muhanzhang/DGCNN","url":"https://github.com/muhanzhang/DGCNN"},{"title":"Ki-Seki/DGCNN-updated","url":"https://github.com/Ki-Seki/DGCNN-updated"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95610,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GNN (DiffPool)","metrics":{"Accuracy":"76.25%"},"paper_url":"http://arxiv.org/abs/1806.08804v4","paper_title":"Hierarchical Graph Representation Learning with Differentiable Pooling","paper_date":"2018-06-22","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/diffpool"},{"title":"RexYing/diffpool","url":"https://github.com/RexYing/diffpool"},{"title":"RexYing/graph-pooling","url":"https://github.com/RexYing/graph-pooling"},{"title":"AaltoPML/Rethinking-pooling-in-GNNs","url":"https://github.com/AaltoPML/Rethinking-pooling-in-GNNs"},{"title":"gospodima/extended-simgnn","url":"https://github.com/gospodima/extended-simgnn"},{"title":"VoVAllen/diffpool","url":"https://github.com/VoVAllen/diffpool"},{"title":"Tioz90/GCN","url":"https://github.com/Tioz90/GCN"},{"title":"Tioz90/DiffPool","url":"https://github.com/Tioz90/DiffPool"},{"title":"basiralab/reproduciblefedgnn","url":"https://github.com/basiralab/reproduciblefedgnn"},{"title":"basiralab/RG-Select","url":"https://github.com/basiralab/RG-Select"},{"title":"chappers/graph-differential-pooling","url":"https://github.com/chappers/graph-differential-pooling"},{"title":"basiralab/Reproducible-Generative-Learning","url":"https://github.com/basiralab/Reproducible-Generative-Learning"},{"title":"PasqualeAuriemma/GCN-DIFFPOOL","url":"https://github.com/PasqualeAuriemma/GCN-DIFFPOOL"},{"title":"cedric_sanders/masterarbeit","url":"https://gitlab.com/cedric_sanders/masterarbeit"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95611,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"R-GIN + PANDA","metrics":{"Accuracy":"76.17"},"paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","paper_date":"2024-06-06","code_links":[{"title":"jeongwhanchoi/panda","url":"https://github.com/jeongwhanchoi/panda"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95612,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GCN + PANDA","metrics":{"Accuracy":"76"},"paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","paper_date":"2024-06-06","code_links":[{"title":"jeongwhanchoi/panda","url":"https://github.com/jeongwhanchoi/panda"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95613,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"R-GCN + PANDA","metrics":{"Accuracy":"76"},"paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","paper_date":"2024-06-06","code_links":[{"title":"jeongwhanchoi/panda","url":"https://github.com/jeongwhanchoi/panda"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95614,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"k-GNN","metrics":{"Accuracy":"75.9%"},"paper_url":"https://arxiv.org/abs/1810.02244v5","paper_title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","paper_date":"2018-10-04","code_links":[{"title":"chrsmrrs/k-gnn","url":"https://github.com/chrsmrrs/k-gnn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95615,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GIN + PANDA","metrics":{"Accuracy":"75.759"},"paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","paper_date":"2024-06-06","code_links":[{"title":"jeongwhanchoi/panda","url":"https://github.com/jeongwhanchoi/panda"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95616,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DGK","metrics":{"Accuracy":"75.68%"},"paper_url":"https://www.semanticscholar.org/paper/Deep-Graph-Kernels-Yanardag-Vishwanathan/8ccd0adb1a00358ede79f1d9bdcce472dc1cb8d4","paper_title":"Deep Graph Kernels","paper_date":"2015-08-10","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95617,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Graph-JEPA","metrics":{"Accuracy":"75.67%"},"paper_url":"https://arxiv.org/abs/2309.16014v3","paper_title":"Graph-level Representation Learning with Joint-Embedding Predictive Architectures","paper_date":"2023-09-27","code_links":[{"title":"geriskenderi/graph-jepa","url":"https://github.com/geriskenderi/graph-jepa"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95618,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"TREE-G","metrics":{"Accuracy":"75.6"},"paper_url":"https://arxiv.org/abs/2207.02760v5","paper_title":"TREE-G: Decision Trees Contesting Graph Neural Networks","paper_date":"2022-07-06","code_links":[{"title":"mayabechlerspeicher/tree-g","url":"https://github.com/mayabechlerspeicher/tree-g"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95619,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GCN","metrics":{"Accuracy":"75.536±1.622"},"paper_url":"http://arxiv.org/abs/1609.02907v4","paper_title":"Semi-Supervised Classification with Graph Convolutional Networks","paper_date":"2016-09-09","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn"},{"title":"tkipf/gcn","url":"https://github.com/tkipf/gcn"},{"title":"tkipf/pygcn","url":"https://github.com/tkipf/pygcn"},{"title":"tkipf/keras-gcn","url":"https://github.com/tkipf/keras-gcn"},{"title":"meliketoy/graph-cnn.pytorch","url":"https://github.com/meliketoy/graph-cnn.pytorch"},{"title":"deepchem/moleculenet","url":"https://github.com/deepchem/moleculenet"},{"title":"IllinoisGraphBenchmark/IGB-Datasets","url":"https://github.com/IllinoisGraphBenchmark/IGB-Datasets"},{"title":"tk-rusch/graphcon","url":"https://github.com/tk-rusch/graphcon"},{"title":"jiangboahu/glcn-tf","url":"https://github.com/jiangboahu/glcn-tf"},{"title":"alexOarga/haiku-geometric","url":"https://github.com/alexOarga/haiku-geometric"},{"title":"mahsa91/RA-GCN","url":"https://github.com/mahsa91/RA-GCN"},{"title":"fanzhenliu/dagad","url":"https://github.com/fanzhenliu/dagad"},{"title":"emartinezs44/SparkGCN","url":"https://github.com/emartinezs44/SparkGCN"},{"title":"yanhuchen/quantum-graph-convolutional-network","url":"https://github.com/yanhuchen/quantum-graph-convolutional-network"},{"title":"HamzaGbada/GCN-Numpy","url":"https://github.com/HamzaGbada/GCN-Numpy"},{"title":"nnaakkaaii/g2-MLP","url":"https://github.com/nnaakkaaii/g2-MLP"},{"title":"basiralab/RG-Select","url":"https://github.com/basiralab/RG-Select"},{"title":"basiralab/reproduciblefedgnn","url":"https://github.com/basiralab/reproduciblefedgnn"},{"title":"cybermonic/cage-4-submission","url":"https://github.com/cybermonic/cage-4-submission"},{"title":"mahsa91/GKD","url":"https://github.com/mahsa91/GKD"},{"title":"selmiss/gp-tlstgcn","url":"https://github.com/selmiss/gp-tlstgcn"},{"title":"hazdzz/GCN","url":"https://github.com/hazdzz/GCN"},{"title":"abi-kothapalli/minimumdominatingsets","url":"https://github.com/abi-kothapalli/minimumdominatingsets"},{"title":"tuananh0305/GCN_ANOMALY_DETECTION","url":"https://github.com/tuananh0305/GCN_ANOMALY_DETECTION"},{"title":"basiralab/Reproducible-Generative-Learning","url":"https://github.com/basiralab/Reproducible-Generative-Learning"},{"title":"negarhdr/PGCN","url":"https://github.com/negarhdr/PGCN"},{"title":"ZeinabRahbar/Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS","url":"https://github.com/ZeinabRahbar/Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS"},{"title":"bcsrn/gcn","url":"https://github.com/bcsrn/gcn"},{"title":"yCobanoglu/infinite-width-gnns","url":"https://github.com/yCobanoglu/infinite-width-gnns"},{"title":"sunxiaobei/mindspore_gallery","url":"https://github.com/sunxiaobei/mindspore_gallery/tree/main/grl/gcn"},{"title":"kiharalab/gnn_pocket","url":"https://github.com/kiharalab/gnn_pocket"},{"title":"ykrmm/sota_gnn","url":"https://github.com/ykrmm/sota_gnn"},{"title":"mahequn001/graphlearning","url":"https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gcn"},{"title":"alililia/ms_contrib","url":"https://github.com/alililia/ms_contrib/tree/main/ascend_gcn"},{"title":"alililia/ms_contrib","url":"https://github.com/alililia/ms_contrib/tree/main/gpu_gcn"},{"title":"30Aniket/Graph-Convolutional-Networks-GCNs","url":"https://github.com/30Aniket/Graph-Convolutional-Networks-GCNs"},{"title":"zhiqiang00/Hon-GCN","url":"https://github.com/zhiqiang00/Hon-GCN"},{"title":"clin366/pygcn","url":"https://github.com/clin366/pygcn"},{"title":"nieci2024/pyglcn","url":"https://github.com/nieci2024/pyglcn"},{"title":"mahendrathapa/graph-convolution-network","url":"https://github.com/mahendrathapa/graph-convolution-network"},{"title":"yangjun1994/CAGCN","url":"https://github.com/yangjun1994/CAGCN"},{"title":"darnbi/pygcn","url":"https://github.com/darnbi/pygcn"},{"title":"1075225782/GCN","url":"https://github.com/1075225782/GCN"},{"title":"udlf/wsef","url":"https://github.com/udlf/wsef"},{"title":"thanhtrunghuynh93/pygcn","url":"https://github.com/thanhtrunghuynh93/pygcn"},{"title":"KimMeen/GCN","url":"https://github.com/KimMeen/GCN"},{"title":"LouisDumont/GCN---re-implementation","url":"https://github.com/LouisDumont/GCN---re-implementation"},{"title":"bepping/non-oversmoothing-gcns","url":"https://github.com/bepping/non-oversmoothing-gcns"},{"title":"toinesayan/node-classification-and-label-dependencies","url":"https://github.com/toinesayan/node-classification-and-label-dependencies"},{"title":"HoganZhang/pygcn_python3","url":"https://github.com/HoganZhang/pygcn_python3"},{"title":"asif-salim/GCN-Graph_Convolutional_Network","url":"https://github.com/asif-salim/GCN-Graph_Convolutional_Network"},{"title":"2023-MindSpore-1/ms-code-11","url":"https://github.com/2023-MindSpore-1/ms-code-11/tree/main/gcn"},{"title":"ajbisberg/gcn","url":"https://github.com/ajbisberg/gcn"},{"title":"lipingcoding/pygcn","url":"https://github.com/lipingcoding/pygcn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95620,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GIN","metrics":{"Accuracy":"75.536±1.851"},"paper_url":"http://arxiv.org/abs/1810.00826v3","paper_title":"How Powerful are Graph Neural Networks?","paper_date":"2018-10-01","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/gin"},{"title":"weihua916/powerful-gnns","url":"https://github.com/weihua916/powerful-gnns"},{"title":"lukecavabarrett/pna","url":"https://github.com/lukecavabarrett/pna"},{"title":"gospodima/extended-simgnn","url":"https://github.com/gospodima/extended-simgnn"},{"title":"calciver/Graph-Isomorphism-Networks","url":"https://github.com/calciver/Graph-Isomorphism-Networks"},{"title":"yuwvandy/g2gnn","url":"https://github.com/yuwvandy/g2gnn"},{"title":"PurdueMINDS/RelationalPooling","url":"https://github.com/PurdueMINDS/RelationalPooling"},{"title":"alexOarga/haiku-geometric","url":"https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gin_conv.py"},{"title":"karolismart/dropgnn","url":"https://github.com/karolismart/dropgnn"},{"title":"egyptdj/graph-neural-mapping","url":"https://github.com/egyptdj/graph-neural-mapping"},{"title":"guillaumejaume/tuto-dl-on-graphs","url":"https://github.com/guillaumejaume/tuto-dl-on-graphs"},{"title":"pfnet-research/treewidth-prediction","url":"https://github.com/pfnet-research/treewidth-prediction"},{"title":"gmum/umwpl2021","url":"https://github.com/gmum/umwpl2021"},{"title":"zhliping/Deep-Learning","url":"https://github.com/zhliping/Deep-Learning"},{"title":"k4my4r/ECG-Classification","url":"https://github.com/k4my4r/ECG-Classification"},{"title":"igorsterner/commute-opt-gnn","url":"https://github.com/igorsterner/commute-opt-gnn"},{"title":"mfmceneaney/Lambda-GNNs","url":"https://github.com/mfmceneaney/Lambda-GNNs"},{"title":"willy-b/tiny-GIN-for-ogbg-molhiv","url":"https://github.com/willy-b/tiny-GIN-for-ogbg-molhiv"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95621,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"CT-Layer","metrics":{"Accuracy":"75.38%"},"paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","paper_date":"2022-06-15","code_links":[{"title":"ellisalicante/GraphRewiring-Tutorial","url":"https://github.com/ellisalicante/GraphRewiring-Tutorial"},{"title":"AdrianArnaiz/DiffWire","url":"https://github.com/AdrianArnaiz/DiffWire"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95622,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"P-WL-UC","metrics":{"Accuracy":"75.36%"},"paper_url":"http://proceedings.mlr.press/v97/rieck19a.html","paper_title":"A Persistent Weisfeiler–Lehman Procedure for Graph Classification","paper_date":"2019-06-09","code_links":[{"title":"BorgwardtLab/P-WL","url":"https://github.com/BorgwardtLab/P-WL"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95623,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GAP-Layer (Ncut)","metrics":{"Accuracy":"75.34%"},"paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","paper_date":"2022-06-15","code_links":[{"title":"ellisalicante/GraphRewiring-Tutorial","url":"https://github.com/ellisalicante/GraphRewiring-Tutorial"},{"title":"AdrianArnaiz/DiffWire","url":"https://github.com/AdrianArnaiz/DiffWire"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95624,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DiffPool","metrics":{"Accuracy":"75.1%"},"paper_url":"http://arxiv.org/abs/1903.02428v3","paper_title":"Fast Graph Representation Learning with PyTorch Geometric","paper_date":"2019-03-06","code_links":[{"title":"rusty1s/pytorch_geometric","url":"https://github.com/rusty1s/pytorch_geometric"},{"title":"leojklarner/gauche","url":"https://github.com/leojklarner/gauche"},{"title":"ncfrey/litmatter","url":"https://github.com/ncfrey/litmatter"},{"title":"long-9621/splinecnn","url":"https://github.com/long-9621/splinecnn"},{"title":"luxtu/OCTA-graph","url":"https://github.com/luxtu/OCTA-graph"},{"title":"NMADALI97/French-web-domain-classification","url":"https://github.com/NMADALI97/French-web-domain-classification"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95625,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DGCNN","metrics":{"Accuracy":"75.1%"},"paper_url":"http://arxiv.org/abs/1712.03563v1","paper_title":"DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model","paper_date":"2017-12-10","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95626,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GMT","metrics":{"Accuracy":"75.09%"},"paper_url":"https://arxiv.org/abs/2102.11533v4","paper_title":"Accurate Learning of Graph Representations with Graph Multiset Pooling","paper_date":"2021-02-23","code_links":[{"title":"JinheonBaek/GMT","url":"https://github.com/JinheonBaek/GMT"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95627,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GAP-Layer (Rcut)","metrics":{"Accuracy":"75.03%"},"paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","paper_date":"2022-06-15","code_links":[{"title":"ellisalicante/GraphRewiring-Tutorial","url":"https://github.com/ellisalicante/GraphRewiring-Tutorial"},{"title":"AdrianArnaiz/DiffWire","url":"https://github.com/AdrianArnaiz/DiffWire"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95628,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"PiNet (Learned p and q)","metrics":{"Accuracy":"75%"},"paper_url":"https://arxiv.org/abs/1905.03046v1","paper_title":"PiNet: A Permutation Invariant Graph Neural Network for Graph Classification","paper_date":"2019-05-08","code_links":[{"title":"meltzerpete/PiNet","url":"https://github.com/meltzerpete/PiNet"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95629,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DiffWire","metrics":{"Accuracy":"74.91%"},"paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","paper_date":"2022-06-15","code_links":[{"title":"ellisalicante/GraphRewiring-Tutorial","url":"https://github.com/ellisalicante/GraphRewiring-Tutorial"},{"title":"AdrianArnaiz/DiffWire","url":"https://github.com/AdrianArnaiz/DiffWire"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95630,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GDL-g (SP)","metrics":{"Accuracy":"74.86"},"paper_url":"https://arxiv.org/abs/2102.06555v2","paper_title":"Online Graph Dictionary Learning","paper_date":"2021-02-12","code_links":[{"title":"cedricvincentcuaz/GDL","url":"https://github.com/cedricvincentcuaz/GDL"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95631,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"VRGC","metrics":{"Accuracy":"74.8%"},"paper_url":"https://arxiv.org/abs/1902.02721v4","paper_title":"Variational Recurrent Neural Networks for Graph Classification","paper_date":"2019-02-07","code_links":[{"title":"edouardpineau/Variational-Recurrent-Neural-Networks-for-Graph-Classification","url":"https://github.com/edouardpineau/Variational-Recurrent-Neural-Networks-for-Graph-Classification"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95632,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"LDP + distance","metrics":{"Accuracy":"74.7%"},"paper_url":"https://arxiv.org/abs/1811.03508v3","paper_title":"A simple yet effective baseline for non-attributed graph classification","paper_date":"2018-11-08","code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"Chen-Cai-OSU/LDP","url":"https://github.com/Chen-Cai-OSU/LDP"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95633,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"δ-2-LWL","metrics":{"Accuracy":"74.60%"},"paper_url":"https://arxiv.org/abs/1904.01543v3","paper_title":"Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings","paper_date":"2019-04-02","code_links":[{"title":"chrsmrrs/sparsewl","url":"https://github.com/chrsmrrs/sparsewl"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95634,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"FGW sp","metrics":{"Accuracy":"74.55%"},"paper_url":"https://arxiv.org/abs/1805.09114v3","paper_title":"Optimal Transport for structured data with application on graphs","paper_date":"2018-05-23","code_links":[{"title":"rflamary/POT","url":"https://github.com/rflamary/POT"},{"title":"tvayer/FGW","url":"https://github.com/tvayer/FGW"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95635,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"WWL","metrics":{"Accuracy":"74.28%"},"paper_url":"https://arxiv.org/abs/1906.01277v2","paper_title":"Wasserstein Weisfeiler-Lehman Graph Kernels","paper_date":"2019-06-04","code_links":[{"title":"BorgwardtLab/WWL","url":"https://github.com/BorgwardtLab/WWL"},{"title":"wenbintum/wwl-gpr","url":"https://github.com/wenbintum/wwl-gpr"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95636,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"RW","metrics":{"Accuracy":"74.22%"},"paper_url":"https://doi.org/10.1016/S0022-2836(03)00628-4","paper_title":"Distinguishing Enzyme Structures from Non-enzymes Without Alignments","paper_date":"2003-07-18","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95637,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"ASAP","metrics":{"Accuracy":"74.19%"},"paper_url":"https://arxiv.org/abs/1911.07979v3","paper_title":"ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations","paper_date":"2019-11-18","code_links":[{"title":"malllabiisc/ASAP","url":"https://github.com/malllabiisc/ASAP"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95638,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"BC + Capsules","metrics":{"Accuracy":"74.1%"},"paper_url":"http://arxiv.org/abs/1902.08399v1","paper_title":"Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations","paper_date":"2019-02-22","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95639,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"LDP + Labels","metrics":{"Accuracy":"73.7%"},"paper_url":"https://arxiv.org/abs/1811.03508v3","paper_title":"A simple yet effective baseline for non-attributed graph classification","paper_date":"2018-11-08","code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"Chen-Cai-OSU/LDP","url":"https://github.com/Chen-Cai-OSU/LDP"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95640,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"DiffPool","metrics":{"Accuracy":"73.7%"},"paper_url":"https://arxiv.org/abs/1912.09893v3","paper_title":"A Fair Comparison of Graph Neural Networks for Graph Classification","paper_date":"2019-12-20","code_links":[{"title":"diningphil/gnn-comparison","url":"https://github.com/diningphil/gnn-comparison"},{"title":"diningphil/CGMM","url":"https://github.com/diningphil/CGMM"},{"title":"FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks","url":"https://github.com/FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks"},{"title":"diningphil/icgmm","url":"https://github.com/diningphil/icgmm"},{"title":"toinesayan/node-classification-and-label-dependencies","url":"https://github.com/toinesayan/node-classification-and-label-dependencies"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95641,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"SF + RFC","metrics":{"Accuracy":"73.6%"},"paper_url":"http://arxiv.org/abs/1810.09155v2","paper_title":"A Simple Baseline Algorithm for Graph Classification","paper_date":"2018-10-22","code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"edouardpineau/A-simple-baseline-algorithm-for-graph-classification","url":"https://github.com/edouardpineau/A-simple-baseline-algorithm-for-graph-classification"},{"title":"edouardpineau/Using-Laplacian-Spectrum-as-Graph-Feature-Representation","url":"https://github.com/edouardpineau/Using-Laplacian-Spectrum-as-Graph-Feature-Representation"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95642,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"EdgePool w GraphSAGE","metrics":{"Accuracy":"73.5%"},"paper_url":"https://arxiv.org/abs/1905.10990v1","paper_title":"Edge Contraction Pooling for Graph Neural Networks","paper_date":"2019-05-27","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95643,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Graph2Vec","metrics":{"Accuracy":"73.3%"},"paper_url":"https://arxiv.org/abs/1910.11436v3","paper_title":"Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling","paper_date":"2019-10-24","code_links":[{"title":"danielegrattarola/decimation-pooling","url":"https://github.com/danielegrattarola/decimation-pooling"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95644,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"graph2vec","metrics":{"Accuracy":"73.3% ± 2.05%"},"paper_url":"http://arxiv.org/abs/1707.05005v1","paper_title":"graph2vec: Learning Distributed Representations of Graphs","paper_date":"2017-07-17","code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"benedekrozemberczki/graph2vec","url":"https://github.com/benedekrozemberczki/graph2vec"},{"title":"MLDroid/graph2vec_tf","url":"https://github.com/MLDroid/graph2vec_tf"},{"title":"paulmorio/geo2dr","url":"https://github.com/paulmorio/geo2dr"},{"title":"compnet/pang","url":"https://github.com/compnet/pang"},{"title":"soumavaghosh/graph2vec","url":"https://github.com/soumavaghosh/graph2vec"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95645,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GraphSAGE","metrics":{"Accuracy":"73%"},"paper_url":"https://arxiv.org/abs/1912.09893v3","paper_title":"A Fair Comparison of Graph Neural Networks for Graph Classification","paper_date":"2019-12-20","code_links":[{"title":"diningphil/gnn-comparison","url":"https://github.com/diningphil/gnn-comparison"},{"title":"diningphil/CGMM","url":"https://github.com/diningphil/CGMM"},{"title":"FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks","url":"https://github.com/FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks"},{"title":"diningphil/icgmm","url":"https://github.com/diningphil/icgmm"},{"title":"toinesayan/node-classification-and-label-dependencies","url":"https://github.com/toinesayan/node-classification-and-label-dependencies"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95646,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Function Space Pooling","metrics":{"Accuracy":"72.8%"},"paper_url":"https://arxiv.org/abs/1905.06259v2","paper_title":"Function Space Pooling For Graph Convolutional Networks","paper_date":"2019-05-15","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95647,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Local Topological Profile (LTP)","metrics":{"Accuracy":"72.7 ± 4.2","Accuracy (10 fold)":"72.7 ± 4.2"},"paper_url":"https://arxiv.org/abs/2305.00724v1","paper_title":"Strengthening structural baselines for graph classification using Local Topological Profile","paper_date":"2023-05-01","code_links":[{"title":"j-adamczyk/ltp","url":"https://github.com/j-adamczyk/ltp"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95648,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"LDP","metrics":{"Accuracy":"72.7%"},"paper_url":"https://arxiv.org/abs/1811.03508v3","paper_title":"A simple yet effective baseline for non-attributed graph classification","paper_date":"2018-11-08","code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"Chen-Cai-OSU/LDP","url":"https://github.com/Chen-Cai-OSU/LDP"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95649,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"EdgePool","metrics":{"Accuracy":"72.5%"},"paper_url":"https://arxiv.org/abs/1905.10990v1","paper_title":"Edge Contraction Pooling for Graph Neural Networks","paper_date":"2019-05-27","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95650,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"1-NMFPool","metrics":{"Accuracy":"72.1%"},"paper_url":"https://arxiv.org/abs/1909.03287v1","paper_title":"A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks","paper_date":"2019-09-07","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95651,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"SPI-GCN","metrics":{"Accuracy":"72.06%"},"paper_url":"https://hal.archives-ouvertes.fr/hal-02093451/","paper_title":"SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network","paper_date":"2019-04-08","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95652,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"SAGPool_h","metrics":{"Accuracy":"71.86%"},"paper_url":"https://arxiv.org/abs/1904.08082v4","paper_title":"Self-Attention Graph Pooling","paper_date":"2019-04-17","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/sagpool"},{"title":"inyeoplee77/SAGPool","url":"https://github.com/inyeoplee77/SAGPool"},{"title":"walidgeuttala/Synthetic-Benchmark-for-Graph-Classification","url":"https://github.com/walidgeuttala/Synthetic-Benchmark-for-Graph-Classification"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95653,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GK","metrics":{"Accuracy":"71.67%"},"paper_url":"http://proceedings.mlr.press/v5/shervashidze09a.html","paper_title":"Efficient graphlet kernels for large graph comparison","paper_date":"2009-04-16","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95654,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"ApproxRepSet","metrics":{"Accuracy":"70.74%"},"paper_url":"https://arxiv.org/abs/1904.01962v2","paper_title":"Rep the Set: Neural Networks for Learning Set Representations","paper_date":"2019-04-03","code_links":[{"title":"giannisnik/repset","url":"https://github.com/giannisnik/repset"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95655,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"SAGPool_g","metrics":{"Accuracy":"70.04%"},"paper_url":"https://arxiv.org/abs/1904.08082v4","paper_title":"Self-Attention Graph Pooling","paper_date":"2019-04-17","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/sagpool"},{"title":"inyeoplee77/SAGPool","url":"https://github.com/inyeoplee77/SAGPool"},{"title":"walidgeuttala/Synthetic-Benchmark-for-Graph-Classification","url":"https://github.com/walidgeuttala/Synthetic-Benchmark-for-Graph-Classification"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95656,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"Eff.resistance graph  kernel","metrics":{"Accuracy":"65.7% ± 4.2%"},"paper_url":"https://arxiv.org/abs/2206.09379v2","paper_title":"0/1 Deep Neural Networks via Block Coordinate Descent","paper_date":"2022-06-19","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95657,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"G-Tuning","metrics":{"Accuracy (10 fold)":"72.05"},"paper_url":"https://arxiv.org/abs/2312.13583v1","paper_title":"Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns","paper_date":"2023-12-21","code_links":[{"title":"zjunet/G-Tuning","url":"https://github.com/zjunet/G-Tuning"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95658,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"GIN-0","metrics":{"Accuracy":"76,2%"},"paper_url":"http://arxiv.org/abs/1810.00826v3","paper_title":"How Powerful are Graph Neural Networks?","paper_date":"2018-10-01","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/gin"},{"title":"weihua916/powerful-gnns","url":"https://github.com/weihua916/powerful-gnns"},{"title":"lukecavabarrett/pna","url":"https://github.com/lukecavabarrett/pna"},{"title":"gospodima/extended-simgnn","url":"https://github.com/gospodima/extended-simgnn"},{"title":"calciver/Graph-Isomorphism-Networks","url":"https://github.com/calciver/Graph-Isomorphism-Networks"},{"title":"yuwvandy/g2gnn","url":"https://github.com/yuwvandy/g2gnn"},{"title":"PurdueMINDS/RelationalPooling","url":"https://github.com/PurdueMINDS/RelationalPooling"},{"title":"alexOarga/haiku-geometric","url":"https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gin_conv.py"},{"title":"karolismart/dropgnn","url":"https://github.com/karolismart/dropgnn"},{"title":"egyptdj/graph-neural-mapping","url":"https://github.com/egyptdj/graph-neural-mapping"},{"title":"guillaumejaume/tuto-dl-on-graphs","url":"https://github.com/guillaumejaume/tuto-dl-on-graphs"},{"title":"pfnet-research/treewidth-prediction","url":"https://github.com/pfnet-research/treewidth-prediction"},{"title":"gmum/umwpl2021","url":"https://github.com/gmum/umwpl2021"},{"title":"zhliping/Deep-Learning","url":"https://github.com/zhliping/Deep-Learning"},{"title":"k4my4r/ECG-Classification","url":"https://github.com/k4my4r/ECG-Classification"},{"title":"igorsterner/commute-opt-gnn","url":"https://github.com/igorsterner/commute-opt-gnn"},{"title":"mfmceneaney/Lambda-GNNs","url":"https://github.com/mfmceneaney/Lambda-GNNs"},{"title":"willy-b/tiny-GIN-for-ogbg-molhiv","url":"https://github.com/willy-b/tiny-GIN-for-ogbg-molhiv"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122325,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"HGP-SL","metrics":{"Accuracy":"84.91"},"paper_url":"https://arxiv.org/abs/1911.05954v3","paper_title":"Hierarchical Graph Pooling with Structure Learning","paper_date":"2019-11-14","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/hgp_sl"},{"title":"cszhangzhen/HGP-SL","url":"https://github.com/cszhangzhen/HGP-SL"},{"title":"gsl-benchmark/gslb","url":"https://github.com/gsl-benchmark/gslb"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122326,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"rLap (unsupervised)","metrics":{"Accuracy":"84.3"},"paper_url":"https://arxiv.org/abs/2306.04004v1","paper_title":"Randomized Schur Complement Views for Graph Contrastive Learning","paper_date":"2023-06-06","code_links":[{"title":"kvignesh1420/rlap","url":"https://github.com/kvignesh1420/rlap"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122327,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"TFGW ADJ (L=2)","metrics":{"Accuracy":"82.9"},"paper_url":"https://arxiv.org/abs/2205.15733v1","paper_title":"Template based Graph Neural Network with Optimal Transport Distances","paper_date":"2022-05-31","code_links":[{"title":"cedricvincentcuaz/TFGW","url":"https://github.com/cedricvincentcuaz/TFGW"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122328,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"ESA (Edge set attention, no positional encodings)","metrics":{"Accuracy":"82.679±0.799"},"paper_url":"https://arxiv.org/abs/2402.10793v2","paper_title":"An end-to-end attention-based approach for learning on graphs","paper_date":"2024-02-16","code_links":[{"title":"davidbuterez/edge-set-attention","url":"https://github.com/davidbuterez/edge-set-attention"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122329,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"FIT-GNN","metrics":{"Accuracy":"82.1","Inference Time (ms)":"0.0016"},"paper_url":"https://arxiv.org/abs/2410.15001v2","paper_title":"FIT-GNN: Faster Inference Time for GNNs Using Coarsening","paper_date":"2024-10-19","code_links":[{"title":"Roy-Shubhajit/FIT-GNN","url":"https://github.com/Roy-Shubhajit/FIT-GNN"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122330,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DUGNN","metrics":{"Accuracy":"81.70%"},"paper_url":"https://arxiv.org/abs/1909.10086v3","paper_title":"Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning","paper_date":"2019-09-22","code_links":[{"title":"vermaMachineLearning/Universal-Graph-Embedding-Neural-Network","url":"https://github.com/vermaMachineLearning/Universal-Graph-Embedding-Neural-Network"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":1,"source":"archive","tags":[]},{"id":122331,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"MEWISPool","metrics":{"Accuracy":"80.71%"},"paper_url":"https://arxiv.org/abs/2107.01410v1","paper_title":"Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks","paper_date":"2021-07-03","code_links":[{"title":"mewispool/mewispool","url":"https://github.com/mewispool/mewispool"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122332,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"CIN++","metrics":{"Accuracy":"80.5"},"paper_url":"https://arxiv.org/abs/2306.03561v1","paper_title":"CIN++: Enhancing Topological Message Passing","paper_date":"2023-06-06","code_links":[{"title":"twitter-research/cwn","url":"https://github.com/twitter-research/cwn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122333,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"SAEPool","metrics":{"Accuracy":"80.36%"},"paper_url":"https://ieeexplore.ieee.org/abstract/document/9750933","paper_title":"Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization","paper_date":"2022-04-06","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122334,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"MAGPool","metrics":{"Accuracy":"80.36%"},"paper_url":"https://ieeexplore.ieee.org/document/10454077","paper_title":"Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective","paper_date":"2024-03-04","code_links":[{"title":"p-haddadian/MAGPool","url":"https://github.com/p-haddadian/MAGPool"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122335,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"UGT","metrics":{"Accuracy":"80.12 ±0.32"},"paper_url":"https://arxiv.org/abs/2308.09517v1","paper_title":"Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity","paper_date":"2023-08-18","code_links":[{"title":"nslab-cuk/unified-graph-transformer","url":"https://github.com/nslab-cuk/unified-graph-transformer"},{"title":"nslab-cuk/community-aware-graph-transformer","url":"https://github.com/nslab-cuk/community-aware-graph-transformer"},{"title":"nslab-cuk/literalkg","url":"https://github.com/nslab-cuk/literalkg"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122336,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"U2GNN (Unsupervised)","metrics":{"Accuracy":"80.01%"},"paper_url":"https://arxiv.org/abs/1909.11855v9","paper_title":"Universal Graph Transformer Self-Attention Networks","paper_date":"2019-09-26","code_links":[{"title":"daiquocnguyen/Graph-Transformer","url":"https://github.com/daiquocnguyen/Graph-Transformer"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122337,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"sGIN","metrics":{"Accuracy":"78.97%"},"paper_url":"https://arxiv.org/abs/1905.08509v4","paper_title":"Mutual Information Maximization in Graph Neural Networks","paper_date":"2019-05-21","code_links":[{"title":"CODE-SUBMIT/Graph_Neighborhood_1","url":"https://github.com/CODE-SUBMIT/Graph_Neighborhood_1"},{"title":"CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network","url":"https://github.com/CODE-SUBMIT/Neighborhood-Enlargement-in-Graph-Network"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122338,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"QS-CNNs (Quantum Walk)","metrics":{"Accuracy":"78.80%"},"paper_url":"https://doi.org/10.1016/j.patcog.2018.11.002","paper_title":"Quantum-based subgraph convolutional neural networks","paper_date":"2019-04-01","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122339,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"WKPI-kmeans","metrics":{"Accuracy":"78.8%"},"paper_url":"https://arxiv.org/abs/1904.12189v2","paper_title":"Learning metrics for persistence-based summaries and applications for graph classification","paper_date":"2019-04-27","code_links":[{"title":"topology474/WKPI","url":"https://github.com/topology474/WKPI"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122340,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"PIN","metrics":{"Accuracy":"78.8%"},"paper_url":"https://arxiv.org/abs/2308.06838v6","paper_title":"Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes","paper_date":"2023-08-13","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122341,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"hGANet","metrics":{"Accuracy":"78.65%"},"paper_url":"https://arxiv.org/abs/1907.04652v1","paper_title":"Graph Representation Learning via Hard and Channel-Wise Attention Networks","paper_date":"2019-07-05","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/hardgat"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122342,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"U2GNN","metrics":{"Accuracy":"78.53%"},"paper_url":"https://arxiv.org/abs/1909.11855v9","paper_title":"Universal Graph Transformer Self-Attention Networks","paper_date":"2019-09-26","code_links":[{"title":"daiquocnguyen/Graph-Transformer","url":"https://github.com/daiquocnguyen/Graph-Transformer"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122343,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DS-CNNs (Random Walk)","metrics":{"Accuracy":"78.35%"},"paper_url":"https://doi.org/10.1016/j.patcog.2018.11.002","paper_title":"Quantum-based subgraph convolutional neural networks","paper_date":"2019-04-01","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122344,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"cGANet","metrics":{"Accuracy":"78.23%"},"paper_url":"https://arxiv.org/abs/1907.04652v1","paper_title":"Graph Representation Learning via Hard and Channel-Wise Attention Networks","paper_date":"2019-07-05","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/hardgat"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122345,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"CAN","metrics":{"Accuracy":"78.2%"},"paper_url":"https://arxiv.org/abs/2209.08179v1","paper_title":"Cell Attention Networks","paper_date":"2022-09-16","code_links":[{"title":"lrnzgiusti/can","url":"https://github.com/lrnzgiusti/can"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122346,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GANet","metrics":{"Accuracy":"77.92%"},"paper_url":"https://arxiv.org/abs/1907.04652v1","paper_title":"Graph Representation Learning via Hard and Channel-Wise Attention Networks","paper_date":"2019-07-05","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/hardgat"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122347,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GraphStar","metrics":{"Accuracy":"77.90%"},"paper_url":"https://arxiv.org/abs/1906.12330v1","paper_title":"Graph Star Net for Generalized Multi-Task Learning","paper_date":"2019-06-21","code_links":[{"title":"graph-star-team/graph_star","url":"https://github.com/graph-star-team/graph_star"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122348,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"NERO","metrics":{"Accuracy":"77.89%"},"paper_url":"https://link.springer.com/chapter/10.1007/978-3-030-77964-1_18","paper_title":"Relation order histograms as a network embedding tool","paper_date":"2021-06-09","code_links":[{"title":"Prpht/NERO","url":"https://github.com/Prpht/NERO"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122349,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Fea2Fea-s2","metrics":{"Accuracy":"77.8%"},"paper_url":"https://arxiv.org/abs/2106.13061v4","paper_title":"Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks","paper_date":"2021-06-24","code_links":[{"title":"JIAQING-XIE/Fea2Fea","url":"https://github.com/JIAQING-XIE/Fea2Fea"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122350,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DGA","metrics":{"Accuracy":"77.71%"},"paper_url":"https://doi.org/10.1109/ICBK.2018.00033","paper_title":"Discriminative Graph Autoencoder","paper_date":"2018-11-17","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122351,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Graph U-Nets","metrics":{"Accuracy":"77.68%"},"paper_url":"https://arxiv.org/abs/1905.05178v1","paper_title":"Graph U-Nets","paper_date":"2019-05-11","code_links":[{"title":"HongyangGao/gunet","url":"https://github.com/HongyangGao/gunet"},{"title":"HongyangGao/Graph-U-Nets","url":"https://github.com/HongyangGao/Graph-U-Nets"},{"title":"hujilin1229/gUNet","url":"https://github.com/hujilin1229/gUNet"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122352,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GATv2","metrics":{"Accuracy":"77.679±2.187"},"paper_url":"https://arxiv.org/abs/2105.14491v3","paper_title":"How Attentive are Graph Attention Networks?","paper_date":"2021-05-30","code_links":[{"title":"labmlai/annotated_deep_learning_paper_implementations","url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations"},{"title":"rusty1s/pytorch_geometric","url":"https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/nn/conv/gatv2_conv.py"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/blob/master/python/dgl/nn/pytorch/conv/gatv2conv.py"},{"title":"tensorflow/gnn","url":"https://github.com/tensorflow/gnn/blob/main/tensorflow_gnn/docs/api_docs/python/gnn/keras/layers/GATv2.md"},{"title":"tech-srl/how_attentive_are_gats","url":"https://github.com/tech-srl/how_attentive_are_gats"},{"title":"machadoprx/GATv2-TensorFlow2","url":"https://github.com/machadoprx/GATv2-TensorFlow2"},{"title":"mahequn001/graphlearning","url":"https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gatv2"},{"title":"briangodwinlim/sir-gcn","url":"https://github.com/briangodwinlim/sir-gcn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122353,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"PNA","metrics":{"Accuracy":"77.679±3.281"},"paper_url":"https://arxiv.org/abs/2004.05718v5","paper_title":"Principal Neighbourhood Aggregation for Graph Nets","paper_date":"2020-04-12","code_links":[{"title":"rusty1s/pytorch_geometric","url":"https://github.com/rusty1s/pytorch_geometric"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl"},{"title":"lukecavabarrett/pna","url":"https://github.com/lukecavabarrett/pna"},{"title":"Saro00/DGN","url":"https://github.com/Saro00/DGN"},{"title":"alexOarga/haiku-geometric","url":"https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/pna_conv.py"},{"title":"cvignac/SMP","url":"https://github.com/cvignac/SMP"},{"title":"asarigun/GraphMixerNetworks","url":"https://github.com/asarigun/GraphMixerNetworks"},{"title":"changminwu/expandergnn","url":"https://github.com/changminwu/expandergnn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122354,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GIC","metrics":{"Accuracy":"77.65%"},"paper_url":"http://arxiv.org/abs/1811.04393v1","paper_title":"Gaussian-Induced Convolution for Graphs","paper_date":"2018-11-11","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122355,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GIUNet","metrics":{"Accuracy":"77.6%"},"paper_url":"https://www.sciencedirect.com/science/article/abs/pii/S0957417423017827#preview-section-abstract","paper_title":"Graph isomorphism UNet","paper_date":"2023-08-23","code_links":[{"title":"aramiracle/GIUNet","url":"https://github.com/aramiracle/GIUNet"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122356,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GFN-light","metrics":{"Accuracy":"77.44%"},"paper_url":"https://arxiv.org/abs/1905.04579v3","paper_title":"Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification","paper_date":"2019-05-11","code_links":[{"title":"chentingpc/gfn","url":"https://github.com/chentingpc/gfn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122357,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"SEAL-SAGE","metrics":{"Accuracy":"77.26%"},"paper_url":"http://arxiv.org/abs/1904.05003v1","paper_title":"Semi-Supervised Graph Classification: A Hierarchical Graph Perspective","paper_date":"2019-04-10","code_links":[{"title":"benedekrozemberczki/SEAL-CI","url":"https://github.com/benedekrozemberczki/SEAL-CI"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122358,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"PPGN","metrics":{"Accuracy":"77.20%"},"paper_url":"https://arxiv.org/abs/1905.11136v4","paper_title":"Provably Powerful Graph Networks","paper_date":"2019-05-27","code_links":[{"title":"hadarser/ProvablyPowerfulGraphNetworks_torch","url":"https://github.com/hadarser/ProvablyPowerfulGraphNetworks_torch"},{"title":"hadarser/ProvablyPowerfulGraphNetworks","url":"https://github.com/hadarser/ProvablyPowerfulGraphNetworks"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122359,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GraphGPS","metrics":{"Accuracy":"77.143±1.494"},"paper_url":"https://arxiv.org/abs/2205.12454v4","paper_title":"Recipe for a General, Powerful, Scalable Graph Transformer","paper_date":"2022-05-25","code_links":[{"title":"rampasek/GraphGPS","url":"https://github.com/rampasek/GraphGPS"},{"title":"hamed1375/exphormer","url":"https://github.com/hamed1375/exphormer"},{"title":"graphcore/ogb-lsc-pcqm4mv2","url":"https://github.com/graphcore/ogb-lsc-pcqm4mv2"},{"title":"linusbao/MoSE","url":"https://github.com/linusbao/MoSE"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122360,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Weak-supervised ChebyNet","metrics":{"Accuracy":"77.09%"},"paper_url":"https://arxiv.org/abs/1905.02850v3","paper_title":"Understanding Attention and Generalization in Graph Neural Networks","paper_date":"2019-05-08","code_links":[{"title":"bknyaz/graph_nn","url":"https://github.com/bknyaz/graph_nn"},{"title":"bknyaz/graph_attention_pool","url":"https://github.com/bknyaz/graph_attention_pool"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122361,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"SEG-BERT","metrics":{"Accuracy":"77.09%"},"paper_url":"https://arxiv.org/abs/2002.03283v1","paper_title":"Segmented Graph-Bert for Graph Instance Modeling","paper_date":"2020-02-09","code_links":[{"title":"jwzhanggy/SEG-BERT","url":"https://github.com/jwzhanggy/SEG-BERT"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122362,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GAT-GC (f-Scaled)","metrics":{"Accuracy":"76.81%"},"paper_url":"https://arxiv.org/abs/1907.02204v4","paper_title":"Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation","paper_date":"2019-07-04","code_links":[{"title":"zetayue/CPA","url":"https://github.com/zetayue/CPA"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122363,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GAT","metrics":{"Accuracy":"76.786±1.670"},"paper_url":"http://arxiv.org/abs/1710.10903v3","paper_title":"Graph Attention Networks","paper_date":"2017-10-30","code_links":[{"title":"labmlai/annotated_deep_learning_paper_implementations","url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat"},{"title":"PetarV-/GAT","url":"https://github.com/PetarV-/GAT"},{"title":"Diego999/pyGAT","url":"https://github.com/Diego999/pyGAT"},{"title":"gordicaleksa/pytorch-GAT","url":"https://github.com/gordicaleksa/pytorch-GAT"},{"title":"shenweichen/GraphNeuralNetwork","url":"https://github.com/shenweichen/GraphNeuralNetwork"},{"title":"HazyResearch/hgcn","url":"https://github.com/HazyResearch/hgcn"},{"title":"GraphSAINT/GraphSAINT","url":"https://github.com/GraphSAINT/GraphSAINT"},{"title":"danielegrattarola/keras-gat","url":"https://github.com/danielegrattarola/keras-gat"},{"title":"lukecavabarrett/pna","url":"https://github.com/lukecavabarrett/pna"},{"title":"meliketoy/graph-cnn.pytorch","url":"https://github.com/meliketoy/graph-cnn.pytorch"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/gnn/gat"},{"title":"Kaimaoge/IGNNK","url":"https://github.com/Kaimaoge/IGNNK"},{"title":"IllinoisGraphBenchmark/IGB-Datasets","url":"https://github.com/IllinoisGraphBenchmark/IGB-Datasets"},{"title":"arangoml/fastgraphml","url":"https://github.com/arangoml/fastgraphml"},{"title":"zhao-tong/GNNs-easy-to-use","url":"https://github.com/zhao-tong/GNNs-easy-to-use"},{"title":"davidpicard/homm","url":"https://github.com/davidpicard/homm"},{"title":"alexOarga/haiku-geometric","url":"https://github.com/alexOarga/haiku-geometric"},{"title":"snowkylin/gnn","url":"https://github.com/snowkylin/gnn"},{"title":"akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network","url":"https://github.com/akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network"},{"title":"fanzhenliu/dagad","url":"https://github.com/fanzhenliu/dagad"},{"title":"weiyangfb/PyTorchSparseGAT","url":"https://github.com/weiyangfb/PyTorchSparseGAT"},{"title":"HeapHop30/graph-attention-nets","url":"https://github.com/HeapHop30/graph-attention-nets"},{"title":"marblet/GNN_models_pytorch_geometric","url":"https://github.com/marblet/GNN_models_pytorch_geometric"},{"title":"marble0117/GNN_models_pytorch_geometric","url":"https://github.com/marble0117/GNN_models_pytorch_geometric"},{"title":"marble0117/GNN_models_pytorch","url":"https://github.com/marble0117/GNN_models_pytorch"},{"title":"zxhhh97/ABot","url":"https://github.com/zxhhh97/ABot"},{"title":"ds4dm/sGat","url":"https://github.com/ds4dm/sGat"},{"title":"ds4dm/sparse-gcn","url":"https://github.com/ds4dm/sparse-gcn"},{"title":"isotlaboratory/ml4vrp","url":"https://github.com/isotlaboratory/ml4vrp"},{"title":"marblet/gat-pytorch","url":"https://github.com/marblet/gat-pytorch"},{"title":"ydzhang-stormstout/HAT","url":"https://github.com/ydzhang-stormstout/HAT"},{"title":"gcucurull/jax-gat","url":"https://github.com/gcucurull/jax-gat"},{"title":"calciver/Graph-Attention-Networks","url":"https://github.com/calciver/Graph-Attention-Networks"},{"title":"shi27feng/transformers.satisfy","url":"https://github.com/shi27feng/transformers.satisfy"},{"title":"pengsl-lab/mssl","url":"https://github.com/pengsl-lab/mssl"},{"title":"pengsl-lab/mssl2drug","url":"https://github.com/pengsl-lab/mssl2drug"},{"title":"nnaakkaaii/g2-MLP","url":"https://github.com/nnaakkaaii/g2-MLP"},{"title":"gmum/umwpl2021","url":"https://github.com/gmum/umwpl2021"},{"title":"taishan1994/pytorch_gat","url":"https://github.com/taishan1994/pytorch_gat"},{"title":"wan2000/mc-mot","url":"https://github.com/wan2000/mc-mot"},{"title":"basiralab/RG-Select","url":"https://github.com/basiralab/RG-Select"},{"title":"dmeoli/neuro-sat","url":"https://github.com/dmeoli/neuro-sat"},{"title":"liu6zijian/simplified-gcn-model","url":"https://github.com/liu6zijian/simplified-gcn-model"},{"title":"tatp22/pytorch-fast-GAT","url":"https://github.com/tatp22/pytorch-fast-GAT"},{"title":"noahtren/Graph-Attention-Networks-TensorFlow-2","url":"https://github.com/noahtren/Graph-Attention-Networks-TensorFlow-2"},{"title":"yururyuru00/GAT_pyG","url":"https://github.com/yururyuru00/GAT_pyG"},{"title":"qema/orca-py","url":"https://github.com/qema/orca-py"},{"title":"zhanglab-aim/cancer-net","url":"https://github.com/zhanglab-aim/cancer-net"},{"title":"gayanku/FDGATII","url":"https://github.com/gayanku/FDGATII"},{"title":"Yindong-Zhang/myGAT","url":"https://github.com/Yindong-Zhang/myGAT"},{"title":"giuseppefutia/link-prediction-code","url":"https://github.com/giuseppefutia/link-prediction-code"},{"title":"Aveek-Saha/Graph-Attention-Net","url":"https://github.com/Aveek-Saha/Graph-Attention-Net"},{"title":"BIG-S2/keras-gnm","url":"https://github.com/BIG-S2/keras-gnm"},{"title":"mlzxzhou/keras-gnm","url":"https://github.com/mlzxzhou/keras-gnm"},{"title":"basiralab/Reproducible-Generative-Learning","url":"https://github.com/basiralab/Reproducible-Generative-Learning"},{"title":"abhilash1910/GraphAttentionNetworks","url":"https://github.com/abhilash1910/GraphAttentionNetworks"},{"title":"Kristof-Neys/GAT_Neo4j_DGL","url":"https://github.com/Kristof-Neys/GAT_Neo4j_DGL"},{"title":"ak24watch/Graph-Deeplearning-GAT","url":"https://github.com/ak24watch/Graph-Deeplearning-GAT"},{"title":"Negus25/GAT-Mindspore","url":"https://github.com/Negus25/GAT-Mindspore"},{"title":"ChengyuSun/gat","url":"https://github.com/ChengyuSun/gat"},{"title":"joaopedromattos/gnee","url":"https://github.com/joaopedromattos/gnee"},{"title":"mitya8128/experiments_notes","url":"https://github.com/mitya8128/experiments_notes"},{"title":"4pygmalion/GAT_tensorflow","url":"https://github.com/4pygmalion/GAT_tensorflow"},{"title":"vailatuts/SSFG-regularization","url":"https://github.com/vailatuts/SSFG-regularization"},{"title":"handasontam/GAT-with-edgewise-attention","url":"https://github.com/handasontam/GAT-with-edgewise-attention"},{"title":"Mind23-2/MindCode-4","url":"https://github.com/Mind23-2/MindCode-4/tree/main/gat"},{"title":"AngusMonroe/GAT-pytorch","url":"https://github.com/AngusMonroe/GAT-pytorch"},{"title":"fongyk/graph-attention","url":"https://github.com/fongyk/graph-attention"},{"title":"404nofound/gat","url":"https://github.com/404nofound/gat"},{"title":"ColdenChan/GAT_D","url":"https://github.com/ColdenChan/GAT_D"},{"title":"MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints","url":"https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints"},{"title":"bowu/GraphSearch","url":"https://github.com/bowu/GraphSearch"},{"title":"Messham87/COVID_GAT","url":"https://github.com/Messham87/COVID_GAT"},{"title":"zhangbo2008/GAT_network","url":"https://github.com/zhangbo2008/GAT_network"},{"title":"blueberryc/pyGAT","url":"https://github.com/blueberryc/pyGAT"},{"title":"anish-lu-yihe/SVRT-by-GAT","url":"https://github.com/anish-lu-yihe/SVRT-by-GAT"},{"title":"WantingZhao/my_GAT","url":"https://github.com/WantingZhao/my_GAT"},{"title":"dzb1998/pyGAT","url":"https://github.com/dzb1998/pyGAT"},{"title":"subercui/pyGConvAT","url":"https://github.com/subercui/pyGConvAT"},{"title":"YunseobShin/wiki_GAT","url":"https://github.com/YunseobShin/wiki_GAT"},{"title":"MindSpore-scientific-2/code-4","url":"https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU"},{"title":"snownus/COOP","url":"https://github.com/snownus/COOP"},{"title":"ChengSashankh/trying-GAT","url":"https://github.com/ChengSashankh/trying-GAT"},{"title":"Anak2016/GAT","url":"https://github.com/Anak2016/GAT"},{"title":"whut2962575697/gat_sementic_segmentation","url":"https://github.com/whut2962575697/gat_sementic_segmentation"},{"title":"Anou9531/GAT","url":"https://github.com/Anou9531/GAT"},{"title":"PumpkinYing/GAT","url":"https://github.com/PumpkinYing/GAT"},{"title":"galkampel/HyperNetworks","url":"https://github.com/galkampel/HyperNetworks"},{"title":"tlmakinen/GAT-noise","url":"https://github.com/tlmakinen/GAT-noise"},{"title":"TyngJiunKuo/deep-learning-project","url":"https://github.com/TyngJiunKuo/deep-learning-project"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122364,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Deep WL SGN(0,1,2)","metrics":{"Accuracy":"76.78%"},"paper_url":"https://arxiv.org/abs/1903.09022v3","paper_title":"Subgraph Networks with Application to Structural Feature Space Expansion","paper_date":"2019-03-21","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122365,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"EigenGCN-3","metrics":{"Accuracy":"76.60%"},"paper_url":"https://arxiv.org/abs/1904.13107v2","paper_title":"Graph Convolutional Networks with EigenPooling","paper_date":"2019-04-30","code_links":[{"title":"alge24/eigenpooling","url":"https://github.com/alge24/eigenpooling"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122366,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Multigraph ChebNet","metrics":{"Accuracy":"76.5%"},"paper_url":"http://arxiv.org/abs/1811.09595v1","paper_title":"Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules","paper_date":"2018-11-23","code_links":[{"title":"bknyaz/graph_nn","url":"https://github.com/bknyaz/graph_nn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122367,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"WEGL","metrics":{"Accuracy":"76.5%"},"paper_url":"https://arxiv.org/abs/2006.09430v2","paper_title":"Wasserstein Embedding for Graph Learning","paper_date":"2020-06-16","code_links":[{"title":"navid-naderi/WEGL","url":"https://github.com/navid-naderi/WEGL"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122368,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"2-WL-GNN","metrics":{"Accuracy":"76.5"},"paper_url":"https://arxiv.org/abs/2007.00346v2","paper_title":"A Novel Higher-order Weisfeiler-Lehman Graph Convolution","paper_date":"2020-07-01","code_links":[{"title":"Cortys/master-thesis","url":"https://github.com/Cortys/master-thesis"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122369,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GFN","metrics":{"Accuracy":"76.46%"},"paper_url":"https://arxiv.org/abs/1905.04579v3","paper_title":"Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification","paper_date":"2019-05-11","code_links":[{"title":"chentingpc/gfn","url":"https://github.com/chentingpc/gfn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122370,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Shortest-Path Kernel","metrics":{"Accuracy":"76.4%"},"paper_url":"https://arxiv.org/abs/1810.02244v5","paper_title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","paper_date":"2018-10-04","code_links":[{"title":"chrsmrrs/k-gnn","url":"https://github.com/chrsmrrs/k-gnn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122371,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GCAPS-CNN","metrics":{"Accuracy":"76.40%"},"paper_url":"http://arxiv.org/abs/1805.08090v4","paper_title":"Graph Capsule Convolutional Neural Networks","paper_date":"2018-05-21","code_links":[{"title":"vermaMachineLearning/Graph-Capsule-CNN-Networks","url":"https://github.com/vermaMachineLearning/Graph-Capsule-CNN-Networks"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122372,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"WL-OA","metrics":{"Accuracy":"76.4%"},"paper_url":"http://arxiv.org/abs/1606.01141v3","paper_title":"On Valid Optimal Assignment Kernels and Applications to Graph Classification","paper_date":"2016-06-03","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122373,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"MLG","metrics":{"Accuracy":"76.34%"},"paper_url":"http://arxiv.org/abs/1603.06186v2","paper_title":"The Multiscale Laplacian Graph Kernel","paper_date":"2016-03-20","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122374,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DAGCN","metrics":{"Accuracy":"76.33%"},"paper_url":"http://arxiv.org/abs/1904.02278v1","paper_title":"DAGCN: Dual Attention Graph Convolutional Networks","paper_date":"2019-04-04","code_links":[{"title":"dawenzi123/DAGCN","url":"https://github.com/dawenzi123/DAGCN"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122375,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"CORE-SP","metrics":{"Accuracy":"76.31%"},"paper_url":"https://arxiv.org/abs/1904.12218v2","paper_title":"Graph Kernels: A Survey","paper_date":"2019-04-27","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122376,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DropGIN","metrics":{"Accuracy":"76.3%"},"paper_url":"https://arxiv.org/abs/2111.06283v1","paper_title":"DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks","paper_date":"2021-11-11","code_links":[{"title":"karolismart/dropgnn","url":"https://github.com/karolismart/dropgnn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122377,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"CapsGNN","metrics":{"Accuracy":"76.28%"},"paper_url":"https://openreview.net/forum?id=Byl8BnRcYm","paper_title":"Capsule Graph Neural Network","paper_date":"2019-05-01","code_links":[{"title":"benedekrozemberczki/CapsGNN","url":"https://github.com/benedekrozemberczki/CapsGNN"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122378,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DGCNN","metrics":{"Accuracy":"76.26%"},"paper_url":"https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17146","paper_title":"An End-to-End Deep Learning Architecture for Graph Classification","paper_date":"2018-04-29","code_links":[{"title":"muhanzhang/DGCNN","url":"https://github.com/muhanzhang/DGCNN"},{"title":"Ki-Seki/DGCNN-updated","url":"https://github.com/Ki-Seki/DGCNN-updated"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122379,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GNN (DiffPool)","metrics":{"Accuracy":"76.25%"},"paper_url":"http://arxiv.org/abs/1806.08804v4","paper_title":"Hierarchical Graph Representation Learning with Differentiable Pooling","paper_date":"2018-06-22","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/diffpool"},{"title":"RexYing/diffpool","url":"https://github.com/RexYing/diffpool"},{"title":"RexYing/graph-pooling","url":"https://github.com/RexYing/graph-pooling"},{"title":"AaltoPML/Rethinking-pooling-in-GNNs","url":"https://github.com/AaltoPML/Rethinking-pooling-in-GNNs"},{"title":"gospodima/extended-simgnn","url":"https://github.com/gospodima/extended-simgnn"},{"title":"VoVAllen/diffpool","url":"https://github.com/VoVAllen/diffpool"},{"title":"Tioz90/GCN","url":"https://github.com/Tioz90/GCN"},{"title":"Tioz90/DiffPool","url":"https://github.com/Tioz90/DiffPool"},{"title":"basiralab/reproduciblefedgnn","url":"https://github.com/basiralab/reproduciblefedgnn"},{"title":"basiralab/RG-Select","url":"https://github.com/basiralab/RG-Select"},{"title":"chappers/graph-differential-pooling","url":"https://github.com/chappers/graph-differential-pooling"},{"title":"basiralab/Reproducible-Generative-Learning","url":"https://github.com/basiralab/Reproducible-Generative-Learning"},{"title":"PasqualeAuriemma/GCN-DIFFPOOL","url":"https://github.com/PasqualeAuriemma/GCN-DIFFPOOL"},{"title":"cedric_sanders/masterarbeit","url":"https://gitlab.com/cedric_sanders/masterarbeit"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122380,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"R-GIN + PANDA","metrics":{"Accuracy":"76.17"},"paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","paper_date":"2024-06-06","code_links":[{"title":"jeongwhanchoi/panda","url":"https://github.com/jeongwhanchoi/panda"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122381,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GCN + PANDA","metrics":{"Accuracy":"76"},"paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","paper_date":"2024-06-06","code_links":[{"title":"jeongwhanchoi/panda","url":"https://github.com/jeongwhanchoi/panda"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122382,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"R-GCN + PANDA","metrics":{"Accuracy":"76"},"paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","paper_date":"2024-06-06","code_links":[{"title":"jeongwhanchoi/panda","url":"https://github.com/jeongwhanchoi/panda"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122383,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"k-GNN","metrics":{"Accuracy":"75.9%"},"paper_url":"https://arxiv.org/abs/1810.02244v5","paper_title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","paper_date":"2018-10-04","code_links":[{"title":"chrsmrrs/k-gnn","url":"https://github.com/chrsmrrs/k-gnn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122384,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GIN + PANDA","metrics":{"Accuracy":"75.759"},"paper_url":"https://arxiv.org/abs/2406.03671v2","paper_title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","paper_date":"2024-06-06","code_links":[{"title":"jeongwhanchoi/panda","url":"https://github.com/jeongwhanchoi/panda"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122385,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DGK","metrics":{"Accuracy":"75.68%"},"paper_url":"https://www.semanticscholar.org/paper/Deep-Graph-Kernels-Yanardag-Vishwanathan/8ccd0adb1a00358ede79f1d9bdcce472dc1cb8d4","paper_title":"Deep Graph Kernels","paper_date":"2015-08-10","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122386,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Graph-JEPA","metrics":{"Accuracy":"75.67%"},"paper_url":"https://arxiv.org/abs/2309.16014v3","paper_title":"Graph-level Representation Learning with Joint-Embedding Predictive Architectures","paper_date":"2023-09-27","code_links":[{"title":"geriskenderi/graph-jepa","url":"https://github.com/geriskenderi/graph-jepa"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122387,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"TREE-G","metrics":{"Accuracy":"75.6"},"paper_url":"https://arxiv.org/abs/2207.02760v5","paper_title":"TREE-G: Decision Trees Contesting Graph Neural Networks","paper_date":"2022-07-06","code_links":[{"title":"mayabechlerspeicher/tree-g","url":"https://github.com/mayabechlerspeicher/tree-g"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122388,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GCN","metrics":{"Accuracy":"75.536±1.622"},"paper_url":"http://arxiv.org/abs/1609.02907v4","paper_title":"Semi-Supervised Classification with Graph Convolutional Networks","paper_date":"2016-09-09","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn"},{"title":"tkipf/gcn","url":"https://github.com/tkipf/gcn"},{"title":"tkipf/pygcn","url":"https://github.com/tkipf/pygcn"},{"title":"tkipf/keras-gcn","url":"https://github.com/tkipf/keras-gcn"},{"title":"meliketoy/graph-cnn.pytorch","url":"https://github.com/meliketoy/graph-cnn.pytorch"},{"title":"deepchem/moleculenet","url":"https://github.com/deepchem/moleculenet"},{"title":"IllinoisGraphBenchmark/IGB-Datasets","url":"https://github.com/IllinoisGraphBenchmark/IGB-Datasets"},{"title":"tk-rusch/graphcon","url":"https://github.com/tk-rusch/graphcon"},{"title":"jiangboahu/glcn-tf","url":"https://github.com/jiangboahu/glcn-tf"},{"title":"alexOarga/haiku-geometric","url":"https://github.com/alexOarga/haiku-geometric"},{"title":"mahsa91/RA-GCN","url":"https://github.com/mahsa91/RA-GCN"},{"title":"fanzhenliu/dagad","url":"https://github.com/fanzhenliu/dagad"},{"title":"emartinezs44/SparkGCN","url":"https://github.com/emartinezs44/SparkGCN"},{"title":"yanhuchen/quantum-graph-convolutional-network","url":"https://github.com/yanhuchen/quantum-graph-convolutional-network"},{"title":"HamzaGbada/GCN-Numpy","url":"https://github.com/HamzaGbada/GCN-Numpy"},{"title":"nnaakkaaii/g2-MLP","url":"https://github.com/nnaakkaaii/g2-MLP"},{"title":"basiralab/RG-Select","url":"https://github.com/basiralab/RG-Select"},{"title":"basiralab/reproduciblefedgnn","url":"https://github.com/basiralab/reproduciblefedgnn"},{"title":"cybermonic/cage-4-submission","url":"https://github.com/cybermonic/cage-4-submission"},{"title":"mahsa91/GKD","url":"https://github.com/mahsa91/GKD"},{"title":"selmiss/gp-tlstgcn","url":"https://github.com/selmiss/gp-tlstgcn"},{"title":"hazdzz/GCN","url":"https://github.com/hazdzz/GCN"},{"title":"abi-kothapalli/minimumdominatingsets","url":"https://github.com/abi-kothapalli/minimumdominatingsets"},{"title":"tuananh0305/GCN_ANOMALY_DETECTION","url":"https://github.com/tuananh0305/GCN_ANOMALY_DETECTION"},{"title":"basiralab/Reproducible-Generative-Learning","url":"https://github.com/basiralab/Reproducible-Generative-Learning"},{"title":"negarhdr/PGCN","url":"https://github.com/negarhdr/PGCN"},{"title":"ZeinabRahbar/Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS","url":"https://github.com/ZeinabRahbar/Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS"},{"title":"bcsrn/gcn","url":"https://github.com/bcsrn/gcn"},{"title":"yCobanoglu/infinite-width-gnns","url":"https://github.com/yCobanoglu/infinite-width-gnns"},{"title":"sunxiaobei/mindspore_gallery","url":"https://github.com/sunxiaobei/mindspore_gallery/tree/main/grl/gcn"},{"title":"kiharalab/gnn_pocket","url":"https://github.com/kiharalab/gnn_pocket"},{"title":"ykrmm/sota_gnn","url":"https://github.com/ykrmm/sota_gnn"},{"title":"mahequn001/graphlearning","url":"https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gcn"},{"title":"alililia/ms_contrib","url":"https://github.com/alililia/ms_contrib/tree/main/ascend_gcn"},{"title":"alililia/ms_contrib","url":"https://github.com/alililia/ms_contrib/tree/main/gpu_gcn"},{"title":"30Aniket/Graph-Convolutional-Networks-GCNs","url":"https://github.com/30Aniket/Graph-Convolutional-Networks-GCNs"},{"title":"zhiqiang00/Hon-GCN","url":"https://github.com/zhiqiang00/Hon-GCN"},{"title":"clin366/pygcn","url":"https://github.com/clin366/pygcn"},{"title":"nieci2024/pyglcn","url":"https://github.com/nieci2024/pyglcn"},{"title":"mahendrathapa/graph-convolution-network","url":"https://github.com/mahendrathapa/graph-convolution-network"},{"title":"yangjun1994/CAGCN","url":"https://github.com/yangjun1994/CAGCN"},{"title":"darnbi/pygcn","url":"https://github.com/darnbi/pygcn"},{"title":"1075225782/GCN","url":"https://github.com/1075225782/GCN"},{"title":"udlf/wsef","url":"https://github.com/udlf/wsef"},{"title":"thanhtrunghuynh93/pygcn","url":"https://github.com/thanhtrunghuynh93/pygcn"},{"title":"KimMeen/GCN","url":"https://github.com/KimMeen/GCN"},{"title":"LouisDumont/GCN---re-implementation","url":"https://github.com/LouisDumont/GCN---re-implementation"},{"title":"bepping/non-oversmoothing-gcns","url":"https://github.com/bepping/non-oversmoothing-gcns"},{"title":"toinesayan/node-classification-and-label-dependencies","url":"https://github.com/toinesayan/node-classification-and-label-dependencies"},{"title":"HoganZhang/pygcn_python3","url":"https://github.com/HoganZhang/pygcn_python3"},{"title":"asif-salim/GCN-Graph_Convolutional_Network","url":"https://github.com/asif-salim/GCN-Graph_Convolutional_Network"},{"title":"2023-MindSpore-1/ms-code-11","url":"https://github.com/2023-MindSpore-1/ms-code-11/tree/main/gcn"},{"title":"ajbisberg/gcn","url":"https://github.com/ajbisberg/gcn"},{"title":"lipingcoding/pygcn","url":"https://github.com/lipingcoding/pygcn"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122389,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GIN","metrics":{"Accuracy":"75.536±1.851"},"paper_url":"http://arxiv.org/abs/1810.00826v3","paper_title":"How Powerful are Graph Neural Networks?","paper_date":"2018-10-01","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/gin"},{"title":"weihua916/powerful-gnns","url":"https://github.com/weihua916/powerful-gnns"},{"title":"lukecavabarrett/pna","url":"https://github.com/lukecavabarrett/pna"},{"title":"gospodima/extended-simgnn","url":"https://github.com/gospodima/extended-simgnn"},{"title":"calciver/Graph-Isomorphism-Networks","url":"https://github.com/calciver/Graph-Isomorphism-Networks"},{"title":"yuwvandy/g2gnn","url":"https://github.com/yuwvandy/g2gnn"},{"title":"PurdueMINDS/RelationalPooling","url":"https://github.com/PurdueMINDS/RelationalPooling"},{"title":"alexOarga/haiku-geometric","url":"https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gin_conv.py"},{"title":"karolismart/dropgnn","url":"https://github.com/karolismart/dropgnn"},{"title":"egyptdj/graph-neural-mapping","url":"https://github.com/egyptdj/graph-neural-mapping"},{"title":"guillaumejaume/tuto-dl-on-graphs","url":"https://github.com/guillaumejaume/tuto-dl-on-graphs"},{"title":"pfnet-research/treewidth-prediction","url":"https://github.com/pfnet-research/treewidth-prediction"},{"title":"gmum/umwpl2021","url":"https://github.com/gmum/umwpl2021"},{"title":"zhliping/Deep-Learning","url":"https://github.com/zhliping/Deep-Learning"},{"title":"k4my4r/ECG-Classification","url":"https://github.com/k4my4r/ECG-Classification"},{"title":"igorsterner/commute-opt-gnn","url":"https://github.com/igorsterner/commute-opt-gnn"},{"title":"mfmceneaney/Lambda-GNNs","url":"https://github.com/mfmceneaney/Lambda-GNNs"},{"title":"willy-b/tiny-GIN-for-ogbg-molhiv","url":"https://github.com/willy-b/tiny-GIN-for-ogbg-molhiv"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122390,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"CT-Layer","metrics":{"Accuracy":"75.38%"},"paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","paper_date":"2022-06-15","code_links":[{"title":"ellisalicante/GraphRewiring-Tutorial","url":"https://github.com/ellisalicante/GraphRewiring-Tutorial"},{"title":"AdrianArnaiz/DiffWire","url":"https://github.com/AdrianArnaiz/DiffWire"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122391,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"P-WL-UC","metrics":{"Accuracy":"75.36%"},"paper_url":"http://proceedings.mlr.press/v97/rieck19a.html","paper_title":"A Persistent Weisfeiler–Lehman Procedure for Graph Classification","paper_date":"2019-06-09","code_links":[{"title":"BorgwardtLab/P-WL","url":"https://github.com/BorgwardtLab/P-WL"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122392,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GAP-Layer (Ncut)","metrics":{"Accuracy":"75.34%"},"paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","paper_date":"2022-06-15","code_links":[{"title":"ellisalicante/GraphRewiring-Tutorial","url":"https://github.com/ellisalicante/GraphRewiring-Tutorial"},{"title":"AdrianArnaiz/DiffWire","url":"https://github.com/AdrianArnaiz/DiffWire"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122393,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DiffPool","metrics":{"Accuracy":"75.1%"},"paper_url":"http://arxiv.org/abs/1903.02428v3","paper_title":"Fast Graph Representation Learning with PyTorch Geometric","paper_date":"2019-03-06","code_links":[{"title":"rusty1s/pytorch_geometric","url":"https://github.com/rusty1s/pytorch_geometric"},{"title":"leojklarner/gauche","url":"https://github.com/leojklarner/gauche"},{"title":"ncfrey/litmatter","url":"https://github.com/ncfrey/litmatter"},{"title":"long-9621/splinecnn","url":"https://github.com/long-9621/splinecnn"},{"title":"luxtu/OCTA-graph","url":"https://github.com/luxtu/OCTA-graph"},{"title":"NMADALI97/French-web-domain-classification","url":"https://github.com/NMADALI97/French-web-domain-classification"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122394,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DGCNN","metrics":{"Accuracy":"75.1%"},"paper_url":"http://arxiv.org/abs/1712.03563v1","paper_title":"DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model","paper_date":"2017-12-10","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122395,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GMT","metrics":{"Accuracy":"75.09%"},"paper_url":"https://arxiv.org/abs/2102.11533v4","paper_title":"Accurate Learning of Graph Representations with Graph Multiset Pooling","paper_date":"2021-02-23","code_links":[{"title":"JinheonBaek/GMT","url":"https://github.com/JinheonBaek/GMT"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122396,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GAP-Layer (Rcut)","metrics":{"Accuracy":"75.03%"},"paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","paper_date":"2022-06-15","code_links":[{"title":"ellisalicante/GraphRewiring-Tutorial","url":"https://github.com/ellisalicante/GraphRewiring-Tutorial"},{"title":"AdrianArnaiz/DiffWire","url":"https://github.com/AdrianArnaiz/DiffWire"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122397,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"PiNet (Learned p and q)","metrics":{"Accuracy":"75%"},"paper_url":"https://arxiv.org/abs/1905.03046v1","paper_title":"PiNet: A Permutation Invariant Graph Neural Network for Graph Classification","paper_date":"2019-05-08","code_links":[{"title":"meltzerpete/PiNet","url":"https://github.com/meltzerpete/PiNet"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122398,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DiffWire","metrics":{"Accuracy":"74.91%"},"paper_url":"https://arxiv.org/abs/2206.07369v3","paper_title":"DiffWire: Inductive Graph Rewiring via the Lovász Bound","paper_date":"2022-06-15","code_links":[{"title":"ellisalicante/GraphRewiring-Tutorial","url":"https://github.com/ellisalicante/GraphRewiring-Tutorial"},{"title":"AdrianArnaiz/DiffWire","url":"https://github.com/AdrianArnaiz/DiffWire"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122399,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GDL-g (SP)","metrics":{"Accuracy":"74.86"},"paper_url":"https://arxiv.org/abs/2102.06555v2","paper_title":"Online Graph Dictionary Learning","paper_date":"2021-02-12","code_links":[{"title":"cedricvincentcuaz/GDL","url":"https://github.com/cedricvincentcuaz/GDL"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122400,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"VRGC","metrics":{"Accuracy":"74.8%"},"paper_url":"https://arxiv.org/abs/1902.02721v4","paper_title":"Variational Recurrent Neural Networks for Graph Classification","paper_date":"2019-02-07","code_links":[{"title":"edouardpineau/Variational-Recurrent-Neural-Networks-for-Graph-Classification","url":"https://github.com/edouardpineau/Variational-Recurrent-Neural-Networks-for-Graph-Classification"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122401,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"LDP + distance","metrics":{"Accuracy":"74.7%"},"paper_url":"https://arxiv.org/abs/1811.03508v3","paper_title":"A simple yet effective baseline for non-attributed graph classification","paper_date":"2018-11-08","code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"Chen-Cai-OSU/LDP","url":"https://github.com/Chen-Cai-OSU/LDP"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122402,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"δ-2-LWL","metrics":{"Accuracy":"74.60%"},"paper_url":"https://arxiv.org/abs/1904.01543v3","paper_title":"Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings","paper_date":"2019-04-02","code_links":[{"title":"chrsmrrs/sparsewl","url":"https://github.com/chrsmrrs/sparsewl"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122403,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"FGW sp","metrics":{"Accuracy":"74.55%"},"paper_url":"https://arxiv.org/abs/1805.09114v3","paper_title":"Optimal Transport for structured data with application on graphs","paper_date":"2018-05-23","code_links":[{"title":"rflamary/POT","url":"https://github.com/rflamary/POT"},{"title":"tvayer/FGW","url":"https://github.com/tvayer/FGW"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122404,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"WWL","metrics":{"Accuracy":"74.28%"},"paper_url":"https://arxiv.org/abs/1906.01277v2","paper_title":"Wasserstein Weisfeiler-Lehman Graph Kernels","paper_date":"2019-06-04","code_links":[{"title":"BorgwardtLab/WWL","url":"https://github.com/BorgwardtLab/WWL"},{"title":"wenbintum/wwl-gpr","url":"https://github.com/wenbintum/wwl-gpr"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122405,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"RW","metrics":{"Accuracy":"74.22%"},"paper_url":"https://doi.org/10.1016/S0022-2836(03)00628-4","paper_title":"Distinguishing Enzyme Structures from Non-enzymes Without Alignments","paper_date":"2003-07-18","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122406,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"ASAP","metrics":{"Accuracy":"74.19%"},"paper_url":"https://arxiv.org/abs/1911.07979v3","paper_title":"ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations","paper_date":"2019-11-18","code_links":[{"title":"malllabiisc/ASAP","url":"https://github.com/malllabiisc/ASAP"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122407,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"BC + Capsules","metrics":{"Accuracy":"74.1%"},"paper_url":"http://arxiv.org/abs/1902.08399v1","paper_title":"Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations","paper_date":"2019-02-22","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122408,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"LDP + Labels","metrics":{"Accuracy":"73.7%"},"paper_url":"https://arxiv.org/abs/1811.03508v3","paper_title":"A simple yet effective baseline for non-attributed graph classification","paper_date":"2018-11-08","code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"Chen-Cai-OSU/LDP","url":"https://github.com/Chen-Cai-OSU/LDP"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122409,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"DiffPool","metrics":{"Accuracy":"73.7%"},"paper_url":"https://arxiv.org/abs/1912.09893v3","paper_title":"A Fair Comparison of Graph Neural Networks for Graph Classification","paper_date":"2019-12-20","code_links":[{"title":"diningphil/gnn-comparison","url":"https://github.com/diningphil/gnn-comparison"},{"title":"diningphil/CGMM","url":"https://github.com/diningphil/CGMM"},{"title":"FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks","url":"https://github.com/FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks"},{"title":"diningphil/icgmm","url":"https://github.com/diningphil/icgmm"},{"title":"toinesayan/node-classification-and-label-dependencies","url":"https://github.com/toinesayan/node-classification-and-label-dependencies"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122410,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"SF + RFC","metrics":{"Accuracy":"73.6%"},"paper_url":"http://arxiv.org/abs/1810.09155v2","paper_title":"A Simple Baseline Algorithm for Graph Classification","paper_date":"2018-10-22","code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"edouardpineau/A-simple-baseline-algorithm-for-graph-classification","url":"https://github.com/edouardpineau/A-simple-baseline-algorithm-for-graph-classification"},{"title":"edouardpineau/Using-Laplacian-Spectrum-as-Graph-Feature-Representation","url":"https://github.com/edouardpineau/Using-Laplacian-Spectrum-as-Graph-Feature-Representation"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122411,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"EdgePool w GraphSAGE","metrics":{"Accuracy":"73.5%"},"paper_url":"https://arxiv.org/abs/1905.10990v1","paper_title":"Edge Contraction Pooling for Graph Neural Networks","paper_date":"2019-05-27","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122412,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Graph2Vec","metrics":{"Accuracy":"73.3%"},"paper_url":"https://arxiv.org/abs/1910.11436v3","paper_title":"Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling","paper_date":"2019-10-24","code_links":[{"title":"danielegrattarola/decimation-pooling","url":"https://github.com/danielegrattarola/decimation-pooling"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122413,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"graph2vec","metrics":{"Accuracy":"73.3% ± 2.05%"},"paper_url":"http://arxiv.org/abs/1707.05005v1","paper_title":"graph2vec: Learning Distributed Representations of Graphs","paper_date":"2017-07-17","code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"benedekrozemberczki/graph2vec","url":"https://github.com/benedekrozemberczki/graph2vec"},{"title":"MLDroid/graph2vec_tf","url":"https://github.com/MLDroid/graph2vec_tf"},{"title":"paulmorio/geo2dr","url":"https://github.com/paulmorio/geo2dr"},{"title":"compnet/pang","url":"https://github.com/compnet/pang"},{"title":"soumavaghosh/graph2vec","url":"https://github.com/soumavaghosh/graph2vec"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122414,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GraphSAGE","metrics":{"Accuracy":"73%"},"paper_url":"https://arxiv.org/abs/1912.09893v3","paper_title":"A Fair Comparison of Graph Neural Networks for Graph Classification","paper_date":"2019-12-20","code_links":[{"title":"diningphil/gnn-comparison","url":"https://github.com/diningphil/gnn-comparison"},{"title":"diningphil/CGMM","url":"https://github.com/diningphil/CGMM"},{"title":"FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks","url":"https://github.com/FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks"},{"title":"diningphil/icgmm","url":"https://github.com/diningphil/icgmm"},{"title":"toinesayan/node-classification-and-label-dependencies","url":"https://github.com/toinesayan/node-classification-and-label-dependencies"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122415,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Function Space Pooling","metrics":{"Accuracy":"72.8%"},"paper_url":"https://arxiv.org/abs/1905.06259v2","paper_title":"Function Space Pooling For Graph Convolutional Networks","paper_date":"2019-05-15","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122416,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Local Topological Profile (LTP)","metrics":{"Accuracy":"72.7 ± 4.2","Accuracy (10 fold)":"72.7 ± 4.2"},"paper_url":"https://arxiv.org/abs/2305.00724v1","paper_title":"Strengthening structural baselines for graph classification using Local Topological Profile","paper_date":"2023-05-01","code_links":[{"title":"j-adamczyk/ltp","url":"https://github.com/j-adamczyk/ltp"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122417,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"LDP","metrics":{"Accuracy":"72.7%"},"paper_url":"https://arxiv.org/abs/1811.03508v3","paper_title":"A simple yet effective baseline for non-attributed graph classification","paper_date":"2018-11-08","code_links":[{"title":"benedekrozemberczki/karateclub","url":"https://github.com/benedekrozemberczki/karateclub"},{"title":"Chen-Cai-OSU/LDP","url":"https://github.com/Chen-Cai-OSU/LDP"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122418,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"EdgePool","metrics":{"Accuracy":"72.5%"},"paper_url":"https://arxiv.org/abs/1905.10990v1","paper_title":"Edge Contraction Pooling for Graph Neural Networks","paper_date":"2019-05-27","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122419,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"1-NMFPool","metrics":{"Accuracy":"72.1%"},"paper_url":"https://arxiv.org/abs/1909.03287v1","paper_title":"A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks","paper_date":"2019-09-07","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122420,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"SPI-GCN","metrics":{"Accuracy":"72.06%"},"paper_url":"https://hal.archives-ouvertes.fr/hal-02093451/","paper_title":"SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network","paper_date":"2019-04-08","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122421,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"SAGPool_h","metrics":{"Accuracy":"71.86%"},"paper_url":"https://arxiv.org/abs/1904.08082v4","paper_title":"Self-Attention Graph Pooling","paper_date":"2019-04-17","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/sagpool"},{"title":"inyeoplee77/SAGPool","url":"https://github.com/inyeoplee77/SAGPool"},{"title":"walidgeuttala/Synthetic-Benchmark-for-Graph-Classification","url":"https://github.com/walidgeuttala/Synthetic-Benchmark-for-Graph-Classification"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122422,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GK","metrics":{"Accuracy":"71.67%"},"paper_url":"http://proceedings.mlr.press/v5/shervashidze09a.html","paper_title":"Efficient graphlet kernels for large graph comparison","paper_date":"2009-04-16","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122423,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"ApproxRepSet","metrics":{"Accuracy":"70.74%"},"paper_url":"https://arxiv.org/abs/1904.01962v2","paper_title":"Rep the Set: Neural Networks for Learning Set Representations","paper_date":"2019-04-03","code_links":[{"title":"giannisnik/repset","url":"https://github.com/giannisnik/repset"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122424,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"SAGPool_g","metrics":{"Accuracy":"70.04%"},"paper_url":"https://arxiv.org/abs/1904.08082v4","paper_title":"Self-Attention Graph Pooling","paper_date":"2019-04-17","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/sagpool"},{"title":"inyeoplee77/SAGPool","url":"https://github.com/inyeoplee77/SAGPool"},{"title":"walidgeuttala/Synthetic-Benchmark-for-Graph-Classification","url":"https://github.com/walidgeuttala/Synthetic-Benchmark-for-Graph-Classification"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122425,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"Eff.resistance graph  kernel","metrics":{"Accuracy":"65.7% ± 4.2%"},"paper_url":"https://arxiv.org/abs/2206.09379v2","paper_title":"0/1 Deep Neural Networks via Block Coordinate Descent","paper_date":"2022-06-19","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122426,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"G-Tuning","metrics":{"Accuracy (10 fold)":"72.05"},"paper_url":"https://arxiv.org/abs/2312.13583v1","paper_title":"Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns","paper_date":"2023-12-21","code_links":[{"title":"zjunet/G-Tuning","url":"https://github.com/zjunet/G-Tuning"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122427,"task":"Graph Classification","parent_task":"Classification","dataset":"PROTEINS","model_name":"GIN-0","metrics":{"Accuracy":"76,2%"},"paper_url":"http://arxiv.org/abs/1810.00826v3","paper_title":"How Powerful are Graph Neural Networks?","paper_date":"2018-10-01","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/gin"},{"title":"weihua916/powerful-gnns","url":"https://github.com/weihua916/powerful-gnns"},{"title":"lukecavabarrett/pna","url":"https://github.com/lukecavabarrett/pna"},{"title":"gospodima/extended-simgnn","url":"https://github.com/gospodima/extended-simgnn"},{"title":"calciver/Graph-Isomorphism-Networks","url":"https://github.com/calciver/Graph-Isomorphism-Networks"},{"title":"yuwvandy/g2gnn","url":"https://github.com/yuwvandy/g2gnn"},{"title":"PurdueMINDS/RelationalPooling","url":"https://github.com/PurdueMINDS/RelationalPooling"},{"title":"alexOarga/haiku-geometric","url":"https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gin_conv.py"},{"title":"karolismart/dropgnn","url":"https://github.com/karolismart/dropgnn"},{"title":"egyptdj/graph-neural-mapping","url":"https://github.com/egyptdj/graph-neural-mapping"},{"title":"guillaumejaume/tuto-dl-on-graphs","url":"https://github.com/guillaumejaume/tuto-dl-on-graphs"},{"title":"pfnet-research/treewidth-prediction","url":"https://github.com/pfnet-research/treewidth-prediction"},{"title":"gmum/umwpl2021","url":"https://github.com/gmum/umwpl2021"},{"title":"zhliping/Deep-Learning","url":"https://github.com/zhliping/Deep-Learning"},{"title":"k4my4r/ECG-Classification","url":"https://github.com/k4my4r/ECG-Classification"},{"title":"igorsterner/commute-opt-gnn","url":"https://github.com/igorsterner/commute-opt-gnn"},{"title":"mfmceneaney/Lambda-GNNs","url":"https://github.com/mfmceneaney/Lambda-GNNs"},{"title":"willy-b/tiny-GIN-for-ogbg-molhiv","url":"https://github.com/willy-b/tiny-GIN-for-ogbg-molhiv"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10 fold)\", \"Inference Time (ms)\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":759137,"task":"Graph Classification","parent_task":null,"dataset":"PROTEINS","model_name":"LightTopoGAT","metrics":{"Accuracy":"2.2"},"paper_url":"https://paperswithcode.com/paper/lighttopogat-enhancing-graph-attention-networks-with-topological-features-for-efficient-graph-classification","paper_title":"LightTopoGAT: Enhancing Graph Attention Networks with Topological Features for Efficient Graph Classification","paper_date":"2025-12-15","code_links":[],"metrics_order":null,"area":null,"uses_additional_data":null,"source":"auto","tags":[]}]}