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Node Classification On Non Homophilic 6

Metrics

1:1 Accuracy

Results

Performance results of various models on this benchmark

Paper TitleRepository
ACMII-GCN+++67.5±0.53Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+67.44±0.31Revisiting Heterophily For Graph Neural Networks
ACM-GCN+67.4±0.44Revisiting Heterophily For Graph Neural Networks
ACM-GCN++67.3±0.48Revisiting Heterophily For Graph Neural Networks
H2GCN67.22±0.90Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
APPNP67.21±0.56Predict then Propagate: Graph Neural Networks meet Personalized PageRank
ACMII-GCN67.15±0.41Revisiting Heterophily For Graph Neural Networks
ACM-GCN67.01±0.38Revisiting Heterophily For Graph Neural Networks
GPRGNN66.90±0.50Adaptive Universal Generalized PageRank Graph Neural Network
FAGCN66.86±0.53Beyond Low-frequency Information in Graph Convolutional Networks
MixHop66.80±0.58MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
ACM-SGC-166.67±0.56Revisiting Heterophily For Graph Neural Networks
ACM-GCNII*66.6±0.57Revisiting Heterophily For Graph Neural Networks
MLP-266.55±0.72New Benchmarks for Learning on Non-Homophilous Graphs
ACM-SGC-266.53±0.57Revisiting Heterophily For Graph Neural Networks
GCNII*66.42±0.56Simple and Deep Graph Convolutional Networks
ACM-GCNII66.39±0.56Revisiting Heterophily For Graph Neural Networks
GCNII66.38±0.45Simple and Deep Graph Convolutional Networks
C&S(1hop)64.60±0.57Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
C&S(2hop)64.52±0.62Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
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Node Classification On Non Homophilic 6 | SOTA | HyperAI