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Node Classification On Non Homophilic 4

Metrics

1:1 Accuracy

Results

Performance results of various models on this benchmark

Model Name
1:1 Accuracy
Paper TitleRepository
ACM-Snowball-268.51 ± 1.7Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-368.4 ± 2.05Revisiting Heterophily For Graph Neural Networks
MixHop36.28 ± 10.22MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
ACM-SGC-163.68 ± 1.62Revisiting Heterophily For Graph Neural Networks
SGC-262.67 ± 2.41Simplifying Graph Convolutional Networks
ACMII-Snowball-367.53 ± 2.83Revisiting Heterophily For Graph Neural Networks
ACM-SGC-260.48 ± 1.55Revisiting Heterophily For Graph Neural Networks
GPRGNN67.48 ± 0.40Adaptive Universal Generalized PageRank Graph Neural Network
GraphSAGE62.15 ± 0.42Inductive Representation Learning on Large Graphs
MLP-246.72 ± 0.46Revisiting Heterophily For Graph Neural Networks
ACMII-GCN68.38 ± 1.36Revisiting Heterophily For Graph Neural Networks
GCN+JK64.68 ± 2.85Revisiting Heterophily For Graph Neural Networks
 GAT+JK68.14 ± 1.18Revisiting Heterophily For Graph Neural Networks
ACM-GCNII58.73 ± 2.52Revisiting Heterophily For Graph Neural Networks
FAGCN49.47 ± 2.84Beyond Low-frequency Information in Graph Convolutional Networks
GCN64.18 ± 2.62Semi-Supervised Classification with Graph Convolutional Networks
SGC-164.86 ± 1.81Simplifying Graph Convolutional Networks
BernNet68.29 ± 1.58BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
ACM-GCN++75.23 ± 1.72Revisiting Heterophily For Graph Neural Networks
GCNII*62.8 ± 2.87Simple and Deep Graph Convolutional Networks
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