Node Classification On Penn94

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Accuracy	Paper Title
GPRGCN	81.38 ± 0.16	Adaptive Universal Generalized PageRank Graph Neural Network
GATJK	80.69 ± 0.36	New Benchmarks for Learning on Non-Homophilous Graphs
GCNII	82.92 ± 0.59	Simple and Deep Graph Convolutional Networks
L Prop 2-hop	74.13 ± 0.46	New Benchmarks for Learning on Non-Homophilous Graphs
GNNMoE(GCN-like P)	85.11±0.39	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
DJ-GNN	84.84±0.34	Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters
C&S 2-hop	78.40 ± 3.12	Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
GCNJK	81.63 ± 0.54	New Benchmarks for Learning on Non-Homophilous Graphs
C&S 1-hop	74.28 ± 1.19	Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
L Prop 1-hop	63.21 ± 0.39	New Benchmarks for Learning on Non-Homophilous Graphs
SGC 2-hop	76.09 ± 0.45	Simplifying Graph Convolutional Networks
GloGNN	85.57 ± 0.35	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
SGC 1-hop	66.79 ± 0.27	Simplifying Graph Convolutional Networks
LINK	80.79 ± 0.49	New Benchmarks for Learning on Non-Homophilous Graphs
MixHop	83.47 ± 0.71	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
H2GCN	81.31 ± 0.60	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
MLP	73.61 ± 0.40	New Benchmarks for Learning on Non-Homophilous Graphs
NCGCN	84.74 ± 0.28	Clarify Confused Nodes via Separated Learning
ACM-GCN+	85.05 ± 0.19	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+	84.95 ± 0.43	Revisiting Heterophily For Graph Neural Networks

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