Papers › Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
Siqi Miao, Miaoyuan Liu, Pan Li
Interpretable graph learning is in need as many scientific applications depend on learning models to collect insights from graph-structured data. Previous works mostly focused on using post-hoc approaches to interpret pre-trained models (graph neural networks in particular). They argue against inherently interpretable models because the good interpretability of these models is often at the cost of their prediction accuracy. However, those post-hoc methods often fail to provide stable interpretation and may extract features that are spuriously correlated with the task. In this work, we address these issues by proposing Graph Stochastic Attention (GSAT). Derived from the information bottleneck principle, GSAT injects stochasticity to the attention weights to block the information from task-irrelevant graph components while learning stochasticity-reduced attention to select task-relevant subgraphs for interpretation. The selected subgraphs provably do not contain patterns that are spuriously correlated with the task under some assumptions. Extensive experiments on eight datasets show that GSAT outperforms the state-of-the-art methods by up to 20%↑ in interpretation AUC and 5%↑ in prediction accuracy. Our code is available at https://github.com/Graph-COM/GSAT.
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Code
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Code Syntology ran Syntology
10 samples harvested; 5 ran; 1 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Property Prediction | ogbg-molhiv | GSAT | Ext. data | No | #11 of 43 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molhiv | GSAT | Number of params | 249602 | #11 of 43 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molhiv | GSAT | Test ROC-AUC | 0.8067 ± 0.0950 | #11 of 43 | Archive leaderboard | report |
| Graph Property Prediction | ogbg-molhiv | GSAT | Validation ROC-AUC | 0.8347 ± 0.0031 | #11 of 43 | 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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