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Node Classification On Non Homophilic 13

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1:1 Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname
1:1 Accuracy
Paper TitleRepository
LINKX84.71 ± 0.52Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods-
GCNJK81.63 ± 0.54Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods-
ACM-GCN82.73 ± 0.52Revisiting Heterophily For Graph Neural Networks-
GAT81.53 ± 0.55Graph Attention Networks-
WRGAT74.32 ± 0.53Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns-
APPNP74.33 ± 0.38Predict then Propagate: Graph Neural Networks meet Personalized PageRank-
C&S 1-hop 74.28 ± 1.19Combining Label Propagation and Simple Models Out-performs Graph Neural Networks-
L Prop 2-hop74.13 ± 0.46Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods-
ACMII-GCN++85.95 ± 0.26Revisiting Heterophily For Graph Neural Networks-
MixHop83.47 ± 0.71MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing-
GATJK80.69 ± 0.36Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods-
GloGNN85.57 ± 0.35Finding Global Homophily in Graph Neural Networks When Meeting Heterophily-
H2GCN81.31 ± 0.60Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs-
C&S 2-hop78.40 ± 3.12Combining Label Propagation and Simple Models Out-performs Graph Neural Networks-
MLP73.61 ± 0.40Revisiting Heterophily For Graph Neural Networks-
ACMII-GCN+84.95 ± 0.43Revisiting Heterophily For Graph Neural Networks-
LINK 80.79 ± 0.49Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods-
SGC 2-hop76.09 ± 0.45Simplifying Graph Convolutional Networks-
ACM-GCN+85.05 ± 0.19Revisiting Heterophily For Graph Neural Networks-
GloGNN++85.74 ± 0.42Finding Global Homophily in Graph Neural Networks When Meeting Heterophily-
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Node Classification On Non Homophilic 13 | SOTA | HyperAI