Node Classification On Non Homophilic 14

평가 지표

1:1 Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	1:1 Accuracy	Paper Title
C&S 2-hop	84.94 ± 0.49	Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
GATJK	56.70 ± 2.07	Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
GCN	87.42 ± 0.37	Semi-Supervised Classification with Graph Convolutional Networks
ClenshawGCN	91.69 ± 0.25	Clenshaw Graph Neural Networks
GloGNN	90.66 ± 0.11	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
MixHop	90.58 ± 0.16	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
GPRGCN	90.05 ± 0.31	Adaptive Universal Generalized PageRank Graph Neural Network
MLP	86.68 ± 0.09	Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
ACM-GCN+	91.33 ± 0.11	Revisiting Heterophily For Graph Neural Networks
OptBasisGNN	90.83±0.11	Graph Neural Networks with Learnable and Optimal Polynomial Bases
LINK	73.56 ± 0.14	Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
ACM-GCN++	91.4 ± 0.07	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+	91.13 ± 0.09	Revisiting Heterophily For Graph Neural Networks
SGC 2-hop	82.10 ± 0.14	Simplifying Graph Convolutional Networks
APPNP	85.36 ± 0.62	Predict then Propagate: Graph Neural Networks meet Personalized PageRank
GloGNN++	90.91 ± 0.13	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
ACMII-GCN++	91.01 ± 0.18	Revisiting Heterophily For Graph Neural Networks
GCNII	90.24 ± 0.09	Simple and Deep Graph Convolutional Networks
ACM-GCN	91.44 ± 0.08	Revisiting Heterophily For Graph Neural Networks
L Prop 2-hop	67.04 ± 0.20	Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

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