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Node Classification On Cora 48 32 20 Fixed

Metrics

1:1 Accuracy

Results

Performance results of various models on this benchmark

Paper Title
NLGAT 88.5 ± 1.8Non-Local Graph Neural Networks
GCNII88.37 ± 1.25Simple and Deep Graph Convolutional Networks
GloGNN++88.33 ± 1.09Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
GloGNN88.31 ± 1.13Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
ACMII-GCN++88.25 ± 0.96Revisiting Heterophily For Graph Neural Networks
WRGAT88.20 ± 2.26Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
ACMII-GCN+88.19 ± 1.17Revisiting Heterophily For Graph Neural Networks
ACM-GCN++88.11 ± 0.96Revisiting Heterophily For Graph Neural Networks
NLGCN 88.1 ± 1.0Non-Local Graph Neural Networks
FAGCN88.05 ± 1.57Beyond Low-frequency Information in Graph Convolutional Networks
ACM-GCN+88.05 ± 0.99Revisiting Heterophily For Graph Neural Networks
ACMII-GCN88.01 ± 1.08Revisiting Heterophily For Graph Neural Networks
GGCN87.95 ± 1.05Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
GPRGCN87.95 ± 1.18Adaptive Universal Generalized PageRank Graph Neural Network
H2GCN87.87 ± 1.20Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
ACM-SGC-287.69 ± 1.07Revisiting Heterophily For Graph Neural Networks
MixHop87.61 ± 0.85MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Gen-NSD87.30 ± 1.15Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Diag-NSD87.14 ± 1.06Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
ACM-SGC-186.9 ± 1.38Revisiting Heterophily For Graph Neural Networks
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Node Classification On Cora 48 32 20 Fixed | SOTA | HyperAI