Papers › Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph...
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks
Chenyang Qiu, Guoshun Nan, Tianyu Xiong, Wendi Deng, Di Wang, Zhiyang Teng, Lijuan Sun, Qimei Cui, Xiaofeng Tao
Graph convolution networks (GCNs) are extensively utilized in various graph tasks to mine knowledge from spatial data. Our study marks the pioneering attempt to quantitatively investigate the GCN robustness over omnipresent heterophilic graphs for node classification. We uncover that the predominant vulnerability is caused by the structural out-of-distribution (OOD) issue. This finding motivates us to present a novel method that aims to harden GCNs by automatically learning Latent Homophilic Structures over heterophilic graphs. We term such a methodology as LHS. To elaborate, our initial step involves learning a latent structure by employing a novel self-expressive technique based on multi-node interactions. Subsequently, the structure is refined using a pairwisely constrained dual-view contrastive learning approach. We iteratively perform the above procedure, enabling a GCN model to aggregate information in a homophilic way on heterophilic graphs. Armed with such an adaptable structure, we can properly mitigate the structural OOD threats over heterophilic graphs. Experiments on various benchmarks show the effectiveness of the proposed LHS approach for robust GCNs.
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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 | Actor | LHS | Accuracy | 38.87±1.0 | #5 of 62 | Archive leaderboard | report |
| Node Classification | Chameleon | LHS | Accuracy | 72.31±1.6 | #21 of 61 | Archive leaderboard | report |
| Node Classification | Cornell | LHS | Accuracy | 85.96±5.1 | #13 of 60 | Archive leaderboard | report |
| Node Classification | Squirrel | LHS | Accuracy | 60.27±1.2 | #25 of 59 | Archive leaderboard | report |
| Node Classification | Texas | LHS | Accuracy | 86.32±4.5 | #21 of 62 | Archive leaderboard | report |
| Node Classification | Wisconsin | LHS | Accuracy | 88.32±2.3 | #17 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.
Methods
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