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Node Classification On Genius

评估指标

1:1 Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称
1:1 Accuracy
Paper TitleRepository
GESN91.72 ± 0.08Addressing Heterophily in Node Classification with Graph Echo State Networks-
LINKX-Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods-
ACM-GCN++-Revisiting Heterophily For Graph Neural Networks-
ACMII-GCN++-Revisiting Heterophily For Graph Neural Networks-
ACM-GCN+-Revisiting Heterophily For Graph Neural Networks-
GPRGCN-Adaptive Universal Generalized PageRank Graph Neural Network-
GCNJK-Finding Global Homophily in Graph Neural Networks When Meeting Heterophily-
ACMII-GCN+-Revisiting Heterophily For Graph Neural Networks-
GCNII-Simple and Deep Graph Convolutional Networks-
LINK -New Benchmarks for Learning on Non-Homophilous Graphs-
MixHop-MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing-
Dual-Net GNN-Feature Selection: Key to Enhance Node Classification with Graph Neural Networks
GloGNN-Finding Global Homophily in Graph Neural Networks When Meeting Heterophily-
L Prop 2-hop-New Benchmarks for Learning on Non-Homophilous Graphs-
L Prop 1-hop-New Benchmarks for Learning on Non-Homophilous Graphs-
APPNP-Predict then Propagate: Graph Neural Networks meet Personalized PageRank-
SGC 2-hop-Simplifying Graph Convolutional Networks-
GloGNN++-Finding Global Homophily in Graph Neural Networks When Meeting Heterophily-
C&S 1-hop -Combining Label Propagation and Simple Models Out-performs Graph Neural Networks-
SGC 1-hop-Simplifying Graph Convolutional Networks-
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