Papers › Neighborhood Homophily-Guided Graph Convolutional Network
Neighborhood Homophily-Guided Graph Convolutional Network
Shengbo Gong, Jiajun Zhou, Chenxuan Xie, Qi Xuan
Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks. However, many real-world graphs are heterophilous, challenging the homophily assumption of classical GNNs. To solve the universality problem, many studies deepen networks or concatenate intermediate representations, which does not inherently change neighbor aggregation and introduces noise. Recent studies propose new metrics to characterize the homophily, but rarely consider the correlation of the proposed metrics and models. In this paper, we first design a new metric, Neighborhood Homophily (NH), to measure the label complexity or purity in node neighborhoods. Furthermore, we incorporate the metric into the classical graph convolutional network (GCN) architecture and propose Neighborhood Homophily-based Graph Convolutional Network (NHGCN). In this framework, neighbors are grouped by estimated NH values and aggregated from different channels, and the resulting node predictions are then used in turn to estimate and update NH values. The two processes of metric estimation and model inference are alternately optimized to achieve better node classification. NHGCN achieves top overall performance on both homophilous and heterophilous benchmarks, with an improvement of up to 7.4% compared to the current SOTA methods
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | Film (60%/20%/20% random splits) | NHGCN | 1:1 Accuracy | 43.94 ± 1.14 | #2 of 37 | Archive leaderboard | report |
| Node Classification | PubMed (60%/20%/20% random splits) | NHGCN | 1:1 Accuracy | 91.56 ± 0.50 | #2 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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