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Node Classification On Non Homophilic 2

Metrics

1:1 Accuracy

Results

Performance results of various models on this benchmark

Paper Title
ACM-GCN++96.56 ± 2Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-295.74 ± 2.22Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+95.41 ± 2.82Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-295.25 ± 1.55Revisiting Heterophily For Graph Neural Networks
ACMII-GCN95.08 ± 2.07Revisiting Heterophily For Graph Neural Networks
ACM-GCN+94.92 ± 2.79Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++94.75 ± 2.91Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-394.75 ± 3.09Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-394.75 ± 2.41Revisiting Heterophily For Graph Neural Networks
ACM-SGC-193.61 ± 1.55Revisiting Heterophily For Graph Neural Networks
ACM-SGC-293.44 ± 2.54Revisiting Heterophily For Graph Neural Networks
ACM-GCNII*93.28 ± 2.79Revisiting Heterophily For Graph Neural Networks
BernNet93.12 ± 0.65BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
GPRGNN92.92 ± 0.61Adaptive Universal Generalized PageRank Graph Neural Network
ACM-GCNII92.46 ± 1.97Revisiting Heterophily For Graph Neural Networks
MLP-292.26 ± 0.71Adaptive Universal Generalized PageRank Graph Neural Network
APPNP91.18 ± 0.70Predict then Propagate: Graph Neural Networks meet Personalized PageRank
FAGCN88.85 ± 4.39Beyond Low-frequency Information in Graph Convolutional Networks
GCNII*88.52 ± 3.02Simple and Deep Graph Convolutional Networks
H2GCN85.90 ± 3.53Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
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Node Classification On Non Homophilic 2 | SOTA | HyperAI