{"task":"Node Classification","dataset":"Cora: fixed 20 node per class","metric_names":["Accuracy","Micro F1"],"rows":[{"id":97044,"task":"Node Classification","parent_task":null,"dataset":"Cora: fixed 20 node per class","model_name":"DSGCN","metrics":{"Accuracy":"84.2"},"paper_url":"https://arxiv.org/abs/2003.11702v1","paper_title":"Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks","paper_date":"2020-03-26","code_links":[{"title":"balcilar/Spectral-Designed-Graph-Convolutions","url":"https://github.com/balcilar/Spectral-Designed-Graph-Convolutions"},{"title":"sidneyarcidiacono/UnderstandingGCNs","url":"https://github.com/sidneyarcidiacono/UnderstandingGCNs"}],"metrics_order":"[\"Accuracy\", \"Micro F1\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97045,"task":"Node Classification","parent_task":null,"dataset":"Cora: fixed 20 node per class","model_name":"Self-supervised GraphMAE","metrics":{"Accuracy":"84.2"},"paper_url":"https://arxiv.org/abs/2205.10803v3","paper_title":"GraphMAE: Self-Supervised Masked Graph Autoencoders","paper_date":"2022-05-22","code_links":[{"title":"thudm/graphmae","url":"https://github.com/thudm/graphmae"},{"title":"thudm/graphmae2","url":"https://github.com/thudm/graphmae2"},{"title":"wehos/cellt","url":"https://github.com/wehos/cellt"}],"metrics_order":"[\"Accuracy\", \"Micro F1\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97046,"task":"Node Classification","parent_task":null,"dataset":"Cora: fixed 20 node per class","model_name":"LDS-GNN","metrics":{"Accuracy":"84.1"},"paper_url":"https://arxiv.org/abs/1903.11960v4","paper_title":"Learning Discrete Structures for Graph Neural Networks","paper_date":"2019-03-28","code_links":[{"title":"lucfra/LDS","url":"https://github.com/lucfra/LDS"},{"title":"lucfra/LDS-GNN","url":"https://github.com/lucfra/LDS-GNN"}],"metrics_order":"[\"Accuracy\", \"Micro F1\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97047,"task":"Node Classification","parent_task":null,"dataset":"Cora: fixed 20 node per class","model_name":"SEGCN","metrics":{"Accuracy":"83.5 ± 0.4"},"paper_url":"http://arxiv.org/abs/1809.09925v1","paper_title":"Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning","paper_date":"2018-09-26","code_links":[{"title":"RoyalVane/SEGCN","url":"https://github.com/RoyalVane/SEGCN"}],"metrics_order":"[\"Accuracy\", \"Micro F1\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97048,"task":"Node Classification","parent_task":null,"dataset":"Cora: fixed 20 node per class","model_name":"TREE-G","metrics":{"Accuracy":"83.5"},"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\", \"Micro F1\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97049,"task":"Node Classification","parent_task":null,"dataset":"Cora: fixed 20 node per class","model_name":"SSGC","metrics":{"Accuracy":"83.0"},"paper_url":"https://openreview.net/forum?id=CYO5T-YjWZV","paper_title":"Simple Spectral Graph Convolution","paper_date":"2021-01-01","code_links":[{"title":"allenhaozhu/SSGC","url":"https://github.com/allenhaozhu/SSGC"},{"title":"hazdzz/SSGC","url":"https://github.com/hazdzz/SSGC"}],"metrics_order":"[\"Accuracy\", \"Micro F1\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97050,"task":"Node Classification","parent_task":null,"dataset":"Cora: fixed 20 node per class","model_name":"ScaleNet","metrics":{"Accuracy":"82.3±1.1"},"paper_url":"https://arxiv.org/abs/2411.19392v2","paper_title":"Scale Invariance of Graph Neural Networks","paper_date":"2024-11-28","code_links":[{"title":"qin87/scalenet","url":"https://github.com/qin87/scalenet"}],"metrics_order":"[\"Accuracy\", \"Micro F1\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97051,"task":"Node Classification","parent_task":null,"dataset":"Cora: fixed 20 node per class","model_name":"Graph InfoClust (GIC)","metrics":{"Accuracy":"81.7 ± 1.5"},"paper_url":"https://arxiv.org/abs/2009.06946v1","paper_title":"Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning","paper_date":"2020-09-15","code_links":[{"title":"cmavro/Graph-InfoClust-GIC","url":"https://github.com/cmavro/Graph-InfoClust-GIC"},{"title":"cmavro/HeMI","url":"https://github.com/cmavro/HeMI"}],"metrics_order":"[\"Accuracy\", \"Micro F1\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":97052,"task":"Node Classification","parent_task":null,"dataset":"Cora: fixed 20 node per class","model_name":"PairE","metrics":{"Micro F1":"75.12"},"paper_url":"https://arxiv.org/abs/2203.01564v1","paper_title":"Graph Representation Learning Beyond Node and Homophily","paper_date":"2022-03-03","code_links":[{"title":"syvail/PairE-Graph-Representation-Learning-Beyond-Node-and-Homophily","url":"https://github.com/syvail/PairE-Graph-Representation-Learning-Beyond-Node-and-Homophily"}],"metrics_order":"[\"Accuracy\", \"Micro F1\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]}]}