Papers › Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
Henan Sun, Xunkai Li, Zhengyu Wu, Daohan Su, Rong-Hua Li, Guoren Wang
Recently, graph neural networks (GNNs) have shown prominent performance in semi-supervised node classification by leveraging knowledge from the graph database. However, most existing GNNs follow the homophily assumption, where connected nodes are more likely to exhibit similar feature distributions and the same labels, and such an assumption has proven to be vulnerable in a growing number of practical applications. As a supplement, heterophily reflects dissimilarity in connected nodes, which has gained significant attention in graph learning. To this end, data engineers aim to develop a powerful GNN model that can ensure performance under both homophily and heterophily. Despite numerous attempts, most existing GNNs struggle to achieve optimal node representations due to the constraints of undirected graphs. The neglect of directed edges results in sub-optimal graph representations, thereby hindering the capacity of GNNs. To address this issue, we introduce AMUD, which quantifies the relationship between node profiles and topology from a statistical perspective, offering valuable insights for Adaptively Modeling the natural directed graphs as the Undirected or Directed graph to maximize the benefits from subsequent graph learning. Furthermore, we propose Adaptive Directed Pattern Aggregation (ADPA) as a new directed graph learning paradigm for AMUD. Empirical studies have demonstrated that AMUD guides efficient graph learning. Meanwhile, extensive experiments on 16 benchmark datasets substantiate the impressive performance of ADPA, outperforming baselines by significant margins of 3.96.
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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 | Chameleon | ADPA | Accuracy | 46.2±1.3 | #59 of 61 | Archive leaderboard | report |
| Node Classification | Cornell | ADPA | Accuracy | 82.9±3.0 | #32 of 60 | Archive leaderboard | report |
| Node Classification | Squirrel | ADPA | Accuracy | 45.2±1.3 | #45 of 59 | Archive leaderboard | report |
| Node Classification | Texas | ADPA | Accuracy | 83.8±2.7 | #40 of 62 | Archive leaderboard | report |
| Node Classification | Wisconsin | ADPA | Accuracy | 81.6±3.5 | #50 of 63 | 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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