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Node Classification On Non Homophilic 1

Metrics

1:1 Accuracy

Results

Performance results of various models on this benchmark

Paper Title
ACM-GCN++97.5 ± 1.25Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++97.13 ± 1.68Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-397.00 ± 2.63Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+96.75 ± 1.79Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-296.63 ± 2.24Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-396.62 ± 1.86Revisiting Heterophily For Graph Neural Networks
ACMII-GCN96.62 ± 2.44Revisiting Heterophily For Graph Neural Networks
ACM-GCN+96.5 ± 2.08Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-296.38 ± 2.59Revisiting Heterophily For Graph Neural Networks
ACM-GCN95.75 ± 2.03Revisiting Heterophily For Graph Neural Networks
ACM-GCNII94.63 ± 2.96Revisiting Heterophily For Graph Neural Networks
ACM-GCNII*94.37 ± 2.81Revisiting Heterophily For Graph Neural Networks
ACM-SGC-294.00 ± 2.61Revisiting Heterophily For Graph Neural Networks
MLP-293.87 ± 3.33New Benchmarks for Learning on Non-Homophilous Graphs
GPRGNN93.75 ± 2.37Adaptive Universal Generalized PageRank Graph Neural Network
ACM-SGC-193.25 ± 2.92Revisiting Heterophily For Graph Neural Networks
APPNP92.00 ± 3.59Predict then Propagate: Graph Neural Networks meet Personalized PageRank
FAGCN89.75 ± 6.37Beyond Low-frequency Information in Graph Convolutional Networks
GCNII*89.12 ± 3.06Simple and Deep Graph Convolutional Networks
H2GCN87.5 ± 1.77Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
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Node Classification On Non Homophilic 1 | SOTA | HyperAI