Node Classification On Non Homophilic

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 Title
FAGCN	88.03 ± 5.6	Beyond Low-frequency Information in Graph Convolutional Networks
MLP-2	91.30 ± 0.70	Adaptive Universal Generalized PageRank Graph Neural Network
ACMII-Snowball-2	95.25 ± 1.55	Revisiting Heterophily For Graph Neural Networks
GCNII	89.18 ± 3.96	Simple and Deep Graph Convolutional Networks
SGC-1	70.98 ± 8.39	Simplifying Graph Convolutional Networks
GCN+JK	66.56 ± 13.82	Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-3	94.26 ± 2.57	Revisiting Heterophily For Graph Neural Networks
APPNP	91.80 ± 0.63	Predict then Propagate: Graph Neural Networks meet Personalized PageRank
GAT+JK	74.43 ± 10.24	Revisiting Heterophily For Graph Neural Networks
GAT	76.00 ± 1.01	Graph Attention Networks
MixHop	60.33 ± 28.53	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
H2GCN	86.23 ± 4.71	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
Snowball-3	82.95 ± 2.1	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
GCNII*	90.49 ± 4.45	Simple and Deep Graph Convolutional Networks
ACMII-Snowball-3	93.61 ± 2.79	Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-2	95.08 ± 3.11	Revisiting Heterophily For Graph Neural Networks
GCN	82.46 ± 3.11	Semi-Supervised Classification with Graph Convolutional Networks
ACMII-GCN	95.9 ± 1.83	Revisiting Heterophily For Graph Neural Networks
ACM-SGC-1	93.77 ± 1.91	Revisiting Heterophily For Graph Neural Networks
ACM-GCN	94.75 ± 3.8	Revisiting Heterophily For Graph Neural Networks

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