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Node Classification On Genius

Metrics

1:1 Accuracy

Results

Performance results of various models on this benchmark

Model Name
1:1 Accuracy
Paper TitleRepository
GESN91.72 ± 0.08Addressing Heterophily in Node Classification with Graph Echo State Networks
LINKX-Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
ACM-GCN++-Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++-Revisiting Heterophily For Graph Neural Networks
ACM-GCN+-Revisiting Heterophily For Graph Neural Networks
GPRGCN-Adaptive Universal Generalized PageRank Graph Neural Network
GCNJK-Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
ACMII-GCN+-Revisiting Heterophily For Graph Neural Networks
GCNII-Simple and Deep Graph Convolutional Networks
LINK -New Benchmarks for Learning on Non-Homophilous Graphs
MixHop-MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Dual-Net GNN-Feature Selection: Key to Enhance Node Classification with Graph Neural Networks
GloGNN-Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
L Prop 2-hop-New Benchmarks for Learning on Non-Homophilous Graphs
L Prop 1-hop-New Benchmarks for Learning on Non-Homophilous Graphs
APPNP-Predict then Propagate: Graph Neural Networks meet Personalized PageRank
SGC 2-hop-Simplifying Graph Convolutional Networks
GloGNN++-Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
C&S 1-hop -Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
SGC 1-hop-Simplifying Graph Convolutional Networks
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