{"task":"Graph Classification","dataset":"IMDb-B","metric_names":["Accuracy","Accuracy (10-fold)","Rand index"],"rows":[{"id":95190,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"U2GNN (Unsupervised)","metrics":{"Accuracy":"96.41%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95191,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"ESA (Edge set attention, no positional encodings)","metrics":{"Accuracy":"86.250±0.957"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95192,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GAT","metrics":{"Accuracy":"84.250±2.062"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95193,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"MEWISPool","metrics":{"Accuracy":"82.13%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95194,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GIN","metrics":{"Accuracy":"81.250±3.775"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95195,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"TokenGT","metrics":{"Accuracy":"80.250±3.304"},"paper_url":"https://arxiv.org/abs/2207.02505v2","paper_title":"Pure Transformers are Powerful Graph Learners","paper_date":"2022-07-06","code_links":[{"title":"jw9730/tokengt","url":"https://github.com/jw9730/tokengt"},{"title":"luis-mueller/wl-transformers","url":"https://github.com/luis-mueller/wl-transformers"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95196,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GATv2","metrics":{"Accuracy":"80.000±2.739"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95197,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"G_ResNet","metrics":{"Accuracy":"79.90%"},"paper_url":"http://arxiv.org/abs/1807.02653v1","paper_title":"When Work Matters: Transforming Classical Network Structures to Graph CNN","paper_date":"2018-07-07","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95198,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GCN","metrics":{"Accuracy":"79.500±3.109"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95199,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GraphGPS","metrics":{"Accuracy":"79.250±3.096"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95200,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"DUGNN","metrics":{"Accuracy":"78.70%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":1,"source":"archive","tags":[]},{"id":95201,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"TFGW ADJ (L=2)","metrics":{"Accuracy":"78.3%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95202,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"PNA","metrics":{"Accuracy":"78.000±3.808"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95203,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"sGIN","metrics":{"Accuracy":"77.94%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95204,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"Graphormer","metrics":{"Accuracy":"77.500±2.646"},"paper_url":"https://arxiv.org/abs/2106.05234v5","paper_title":"Do Transformers Really Perform Bad for Graph Representation?","paper_date":"2021-06-09","code_links":[{"title":"microsoft/Graphormer","url":"https://github.com/microsoft/Graphormer"},{"title":"Microsoft/Graphormer","url":"https://github.com/Microsoft/Graphormer"},{"title":"dpstart/graphormer_new","url":"https://github.com/dpstart/graphormer_new"},{"title":"ytchx1999/Graphormer","url":"https://github.com/ytchx1999/Graphormer/tree/main/examples/ogb"},{"title":"yangyucheng000/University","url":"https://github.com/yangyucheng000/University/tree/main/model-2/graphormer"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95205,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"SEG-BERT","metrics":{"Accuracy":"77.2%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95206,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"U2GNN","metrics":{"Accuracy":"77.04%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95207,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"PIN","metrics":{"Accuracy":"76.6%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95208,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GIUNet","metrics":{"Accuracy":"76%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95209,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"Deep WL SGN(0,1,2)","metrics":{"Accuracy":"75.70%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95210,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"DropGIN","metrics":{"Accuracy":"75.7%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95211,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"WKPI-kcenters","metrics":{"Accuracy":"75.4%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95212,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"WEGL","metrics":{"Accuracy":"75.4%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95213,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"FactorGCN","metrics":{"Accuracy":"75.3%","Accuracy (10-fold)":"75.3%"},"paper_url":"https://arxiv.org/abs/2010.05421v1","paper_title":"Factorizable Graph Convolutional Networks","paper_date":"2020-10-12","code_links":[{"title":"ihollywhy/FactorGCN.PyTorch","url":"https://github.com/ihollywhy/FactorGCN.PyTorch"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95214,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GIN-0","metrics":{"Accuracy":"75.1%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95215,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"Local Topological Profile (LTP)","metrics":{"Accuracy":"74.5 ± 4.3","Accuracy (10-fold)":"74.5 ± 4.3"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95216,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"AWE","metrics":{"Accuracy":"74.45%"},"paper_url":"http://arxiv.org/abs/1805.11921v3","paper_title":"Anonymous Walk Embeddings","paper_date":"2018-05-30","code_links":[{"title":"nd7141/AWE","url":"https://github.com/nd7141/AWE"},{"title":"paulmorio/geo2dr","url":"https://github.com/paulmorio/geo2dr"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95217,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"k-GNN","metrics":{"Accuracy":"74.2%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95218,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"Graph-JEPA","metrics":{"Accuracy":"73.68%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95219,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"3-WL Kernel","metrics":{"Accuracy":"73.5%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95220,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GMT","metrics":{"Accuracy":"73.48%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95221,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"δ-2-LWL","metrics":{"Accuracy":"73.4%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95222,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"CapsGNN","metrics":{"Accuracy":"73.10%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95223,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"InfoGraph","metrics":{"Accuracy":"73.03%"},"paper_url":"https://arxiv.org/abs/1908.01000v3","paper_title":"InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization","paper_date":"2019-07-31","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/infograph"},{"title":"fanyun-sun/InfoGraph","url":"https://github.com/fanyun-sun/InfoGraph"},{"title":"yuwvandy/g2gnn","url":"https://github.com/yuwvandy/g2gnn"},{"title":"hengruizhang98/InfoGraph","url":"https://github.com/hengruizhang98/InfoGraph"},{"title":"ycremar/DIG-SSL","url":"https://github.com/ycremar/DIG-SSL"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95224,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GFN","metrics":{"Accuracy":"73.00%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95225,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GFN-light","metrics":{"Accuracy":"73.00%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95226,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"TREE-G","metrics":{"Accuracy":"73%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95227,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GIN-0","metrics":{"Accuracy":"72.8%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95228,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"PPGN","metrics":{"Accuracy":"72.6%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95229,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"2-WL-GNN","metrics":{"Accuracy":"72.2"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95230,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GDL","metrics":{"Accuracy":"72.06%","Rand index":"51.64"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95231,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GCAPS-CNN","metrics":{"Accuracy":"71.69%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95232,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"ApproxRepSet","metrics":{"Accuracy":"71.46%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95233,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"PSCN","metrics":{"Accuracy":"71.00%"},"paper_url":"http://arxiv.org/abs/1605.05273v4","paper_title":"Learning Convolutional Neural Networks for Graphs","paper_date":"2016-05-17","code_links":[{"title":"tvayer/PSCN","url":"https://github.com/tvayer/PSCN"},{"title":"CielAl/PatchySan","url":"https://github.com/CielAl/PatchySan"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95234,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"2D CNN","metrics":{"Accuracy":"70.40%"},"paper_url":"https://arxiv.org/abs/1708.02218v4","paper_title":"Graph Classification with 2D Convolutional Neural Networks","paper_date":"2017-07-29","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95235,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"DGCNN","metrics":{"Accuracy":"70.03%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95236,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"GraphSAGE","metrics":{"Accuracy":"68.8%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95237,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"DGK","metrics":{"Accuracy":"66.96%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95238,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"SPI-GCN","metrics":{"Accuracy":"60.40%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95239,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"DGCNN (sum)","metrics":{"Accuracy":"51.69%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":95240,"task":"Graph Classification","parent_task":null,"dataset":"IMDb-B","model_name":"G-Tuning","metrics":{"Accuracy (10-fold)":"74.30"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121959,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"U2GNN (Unsupervised)","metrics":{"Accuracy":"96.41%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121960,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"ESA (Edge set attention, no positional encodings)","metrics":{"Accuracy":"86.250±0.957"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121961,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GAT","metrics":{"Accuracy":"84.250±2.062"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121962,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"MEWISPool","metrics":{"Accuracy":"82.13%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121963,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GIN","metrics":{"Accuracy":"81.250±3.775"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121964,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"TokenGT","metrics":{"Accuracy":"80.250±3.304"},"paper_url":"https://arxiv.org/abs/2207.02505v2","paper_title":"Pure Transformers are Powerful Graph Learners","paper_date":"2022-07-06","code_links":[{"title":"jw9730/tokengt","url":"https://github.com/jw9730/tokengt"},{"title":"luis-mueller/wl-transformers","url":"https://github.com/luis-mueller/wl-transformers"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121965,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GATv2","metrics":{"Accuracy":"80.000±2.739"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121966,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"G_ResNet","metrics":{"Accuracy":"79.90%"},"paper_url":"http://arxiv.org/abs/1807.02653v1","paper_title":"When Work Matters: Transforming Classical Network Structures to Graph CNN","paper_date":"2018-07-07","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121967,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GCN","metrics":{"Accuracy":"79.500±3.109"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121968,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GraphGPS","metrics":{"Accuracy":"79.250±3.096"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121969,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"DUGNN","metrics":{"Accuracy":"78.70%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":1,"source":"archive","tags":[]},{"id":121970,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"TFGW ADJ (L=2)","metrics":{"Accuracy":"78.3%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121971,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"PNA","metrics":{"Accuracy":"78.000±3.808"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121972,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"sGIN","metrics":{"Accuracy":"77.94%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121973,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"Graphormer","metrics":{"Accuracy":"77.500±2.646"},"paper_url":"https://arxiv.org/abs/2106.05234v5","paper_title":"Do Transformers Really Perform Bad for Graph Representation?","paper_date":"2021-06-09","code_links":[{"title":"microsoft/Graphormer","url":"https://github.com/microsoft/Graphormer"},{"title":"Microsoft/Graphormer","url":"https://github.com/Microsoft/Graphormer"},{"title":"dpstart/graphormer_new","url":"https://github.com/dpstart/graphormer_new"},{"title":"ytchx1999/Graphormer","url":"https://github.com/ytchx1999/Graphormer/tree/main/examples/ogb"},{"title":"yangyucheng000/University","url":"https://github.com/yangyucheng000/University/tree/main/model-2/graphormer"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121974,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"SEG-BERT","metrics":{"Accuracy":"77.2%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121975,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"U2GNN","metrics":{"Accuracy":"77.04%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121976,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"PIN","metrics":{"Accuracy":"76.6%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121977,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GIUNet","metrics":{"Accuracy":"76%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121978,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"Deep WL SGN(0,1,2)","metrics":{"Accuracy":"75.70%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121979,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"DropGIN","metrics":{"Accuracy":"75.7%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121980,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"WKPI-kcenters","metrics":{"Accuracy":"75.4%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121981,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"WEGL","metrics":{"Accuracy":"75.4%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121982,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"FactorGCN","metrics":{"Accuracy":"75.3%","Accuracy (10-fold)":"75.3%"},"paper_url":"https://arxiv.org/abs/2010.05421v1","paper_title":"Factorizable Graph Convolutional Networks","paper_date":"2020-10-12","code_links":[{"title":"ihollywhy/FactorGCN.PyTorch","url":"https://github.com/ihollywhy/FactorGCN.PyTorch"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121983,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GIN-0","metrics":{"Accuracy":"75.1%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121984,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"Local Topological Profile (LTP)","metrics":{"Accuracy":"74.5 ± 4.3","Accuracy (10-fold)":"74.5 ± 4.3"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121985,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"AWE","metrics":{"Accuracy":"74.45%"},"paper_url":"http://arxiv.org/abs/1805.11921v3","paper_title":"Anonymous Walk Embeddings","paper_date":"2018-05-30","code_links":[{"title":"nd7141/AWE","url":"https://github.com/nd7141/AWE"},{"title":"paulmorio/geo2dr","url":"https://github.com/paulmorio/geo2dr"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121986,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"k-GNN","metrics":{"Accuracy":"74.2%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121987,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"Graph-JEPA","metrics":{"Accuracy":"73.68%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121988,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"3-WL Kernel","metrics":{"Accuracy":"73.5%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121989,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GMT","metrics":{"Accuracy":"73.48%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121990,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"δ-2-LWL","metrics":{"Accuracy":"73.4%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121991,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"CapsGNN","metrics":{"Accuracy":"73.10%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121992,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"InfoGraph","metrics":{"Accuracy":"73.03%"},"paper_url":"https://arxiv.org/abs/1908.01000v3","paper_title":"InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization","paper_date":"2019-07-31","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/infograph"},{"title":"fanyun-sun/InfoGraph","url":"https://github.com/fanyun-sun/InfoGraph"},{"title":"yuwvandy/g2gnn","url":"https://github.com/yuwvandy/g2gnn"},{"title":"hengruizhang98/InfoGraph","url":"https://github.com/hengruizhang98/InfoGraph"},{"title":"ycremar/DIG-SSL","url":"https://github.com/ycremar/DIG-SSL"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121993,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GFN","metrics":{"Accuracy":"73.00%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121994,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GFN-light","metrics":{"Accuracy":"73.00%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121995,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"TREE-G","metrics":{"Accuracy":"73%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121996,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GIN-0","metrics":{"Accuracy":"72.8%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121997,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"PPGN","metrics":{"Accuracy":"72.6%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121998,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"2-WL-GNN","metrics":{"Accuracy":"72.2"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":121999,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GDL","metrics":{"Accuracy":"72.06%","Rand index":"51.64"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122000,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GCAPS-CNN","metrics":{"Accuracy":"71.69%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122001,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"ApproxRepSet","metrics":{"Accuracy":"71.46%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122002,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"PSCN","metrics":{"Accuracy":"71.00%"},"paper_url":"http://arxiv.org/abs/1605.05273v4","paper_title":"Learning Convolutional Neural Networks for Graphs","paper_date":"2016-05-17","code_links":[{"title":"tvayer/PSCN","url":"https://github.com/tvayer/PSCN"},{"title":"CielAl/PatchySan","url":"https://github.com/CielAl/PatchySan"}],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122003,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"2D CNN","metrics":{"Accuracy":"70.40%"},"paper_url":"https://arxiv.org/abs/1708.02218v4","paper_title":"Graph Classification with 2D Convolutional Neural Networks","paper_date":"2017-07-29","code_links":[],"metrics_order":"[\"Accuracy\", \"Accuracy (10-fold)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122004,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"DGCNN","metrics":{"Accuracy":"70.03%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122005,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"GraphSAGE","metrics":{"Accuracy":"68.8%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122006,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"DGK","metrics":{"Accuracy":"66.96%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122007,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"SPI-GCN","metrics":{"Accuracy":"60.40%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122008,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"DGCNN (sum)","metrics":{"Accuracy":"51.69%"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":122009,"task":"Graph Classification","parent_task":"Classification","dataset":"IMDb-B","model_name":"G-Tuning","metrics":{"Accuracy (10-fold)":"74.30"},"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)\", \"Rand index\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]}]}