{"task":"Node Classification","dataset":"Cora Full-supervised","metric_names":["Accuracy"],"rows":[{"id":97065,"task":"Node Classification","parent_task":null,"dataset":"Cora Full-supervised","model_name":"GCNII","metrics":{"Accuracy":"88.49%"},"paper_url":"https://arxiv.org/abs/2007.02133v1","paper_title":"Simple and Deep Graph Convolutional Networks","paper_date":"2020-07-04","code_links":[{"title":"chennnM/GCNII","url":"https://github.com/chennnM/GCNII/tree/master/PyG/ogbn-arxiv"},{"title":"chennnM/GCNII","url":"https://github.com/chennnM/GCNII"},{"title":"zhanglab-aim/cancer-net","url":"https://github.com/zhanglab-aim/cancer-net"},{"title":"tyxxzjpdez/GCNII-DropGroups","url":"https://github.com/tyxxzjpdez/GCNII-DropGroups"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97066,"task":"Node Classification","parent_task":null,"dataset":"Cora Full-supervised","model_name":"IncepGCN+DropEdge","metrics":{"Accuracy":"88.2%"},"paper_url":"https://arxiv.org/abs/1907.10903v4","paper_title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification","paper_date":"2019-07-25","code_links":[{"title":"GraphSAINT/GraphSAINT","url":"https://github.com/GraphSAINT/GraphSAINT"},{"title":"GraphSAINT/GraphSAINT","url":"https://github.com/GraphSAINT/GraphSAINT/tree/master/graphsaint/open_graph_benchmark"},{"title":"DropEdge/DropEdge","url":"https://github.com/DropEdge/DropEdge"},{"title":"chr26195/pmlp","url":"https://github.com/chr26195/pmlp"},{"title":"zjunet/dropmessage","url":"https://github.com/zjunet/dropmessage"},{"title":"luckytiger123/dropmessage","url":"https://github.com/luckytiger123/dropmessage"},{"title":"sandl99/KGraph","url":"https://github.com/sandl99/KGraph"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97067,"task":"Node Classification","parent_task":null,"dataset":"Cora Full-supervised","model_name":"FDGATII","metrics":{"Accuracy":"87.7867%"},"paper_url":"https://arxiv.org/abs/2110.11464v2","paper_title":"FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping","paper_date":"2021-10-21","code_links":[{"title":"gayanku/FDGATII","url":"https://github.com/gayanku/FDGATII"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97068,"task":"Node Classification","parent_task":null,"dataset":"Cora Full-supervised","model_name":"ASGCN","metrics":{"Accuracy":"87.44±0.0034%"},"paper_url":"http://arxiv.org/abs/1809.05343v3","paper_title":"Adaptive Sampling Towards Fast Graph Representation Learning","paper_date":"2018-09-14","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/_deprecated/adaptive_sampling"},{"title":"huangwb/AS-GCN","url":"https://github.com/huangwb/AS-GCN"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97069,"task":"Node Classification","parent_task":null,"dataset":"Cora Full-supervised","model_name":"Graph ESN","metrics":{"Accuracy":"86.0±1.0"},"paper_url":"https://arxiv.org/abs/2210.15731v1","paper_title":"Beyond Homophily with Graph Echo State Networks","paper_date":"2022-10-27","code_links":[],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97070,"task":"Node Classification","parent_task":null,"dataset":"Cora Full-supervised","model_name":"FastGCN","metrics":{"Accuracy":"85.00%"},"paper_url":"http://arxiv.org/abs/1801.10247v1","paper_title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","paper_date":"2018-01-30","code_links":[{"title":"matenure/FastGCN","url":"https://github.com/matenure/FastGCN"},{"title":"gkunnan97/fastgcn_pytorch","url":"https://github.com/gkunnan97/fastgcn_pytorch"},{"title":"jiechenjiechen/FastGCN-matlab","url":"https://github.com/jiechenjiechen/FastGCN-matlab"},{"title":"gmancino/fastgcn-pytorch","url":"https://github.com/gmancino/fastgcn-pytorch"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97071,"task":"Node Classification","parent_task":null,"dataset":"Cora Full-supervised","model_name":"GraphSAGE","metrics":{"Accuracy":"82.2%"},"paper_url":"http://arxiv.org/abs/1706.02216v4","paper_title":"Inductive Representation Learning on Large Graphs","paper_date":"2017-06-07","code_links":[{"title":"pyg-team/pytorch_geometric","url":"https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/models/basic_gnn.py"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/graphsage"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin"},{"title":"williamleif/GraphSAGE","url":"https://github.com/williamleif/GraphSAGE"},{"title":"stellargraph/stellargraph","url":"https://github.com/stellargraph/stellargraph"},{"title":"twjiang/graphSAGE-pytorch","url":"https://github.com/twjiang/graphSAGE-pytorch"},{"title":"massquantity/LibRecommender","url":"https://github.com/massquantity/LibRecommender"},{"title":"weiyinwei/mmgcn","url":"https://github.com/weiyinwei/mmgcn"},{"title":"IllinoisGraphBenchmark/IGB-Datasets","url":"https://github.com/IllinoisGraphBenchmark/IGB-Datasets"},{"title":"arangoml/fastgraphml","url":"https://github.com/arangoml/fastgraphml"},{"title":"zxhhh97/ABot","url":"https://github.com/zxhhh97/ABot"},{"title":"isotlaboratory/ml4vrp","url":"https://github.com/isotlaboratory/ml4vrp"},{"title":"weiyinwei/huign","url":"https://github.com/weiyinwei/huign"},{"title":"qema/orca-py","url":"https://github.com/qema/orca-py"},{"title":"pmlg/pytorch_GCN","url":"https://github.com/pmlg/pytorch_GCN"},{"title":"chatterjeeayan/upna","url":"https://github.com/chatterjeeayan/upna"},{"title":"Arvidsmile/InductiveDialogueGraph","url":"https://github.com/Arvidsmile/InductiveDialogueGraph"},{"title":"silent-code/deep-trace","url":"https://github.com/silent-code/deep-trace"},{"title":"erfmah/answering_graph_queries","url":"https://github.com/erfmah/answering_graph_queries"},{"title":"hmcreamer/hackRice19","url":"https://github.com/hmcreamer/hackRice19"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97072,"task":"Node Classification","parent_task":null,"dataset":"Cora Full-supervised","model_name":"NCGCN","metrics":{"Accuracy":"73.42 ± 0.58%"},"paper_url":"https://arxiv.org/abs/2306.02285v6","paper_title":"Clarify Confused Nodes via Separated Learning","paper_date":"2023-06-04","code_links":[{"title":"GISec-Team/NCGNN","url":"https://github.com/GISec-Team/NCGNN"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97073,"task":"Node Classification","parent_task":null,"dataset":"Cora Full-supervised","model_name":"GraphMix (GCN)","metrics":{"Accuracy":"61.8%"},"paper_url":"https://arxiv.org/abs/1909.11715v3","paper_title":"GraphMix: Improved Training of GNNs for Semi-Supervised Learning","paper_date":"2019-09-25","code_links":[{"title":"vikasverma1077/GraphMix","url":"https://github.com/vikasverma1077/GraphMix"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]}]}