What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition
Graph neural networks are widely used for node classification, but they remain vulnerable to out-of-distribution (OOD) shifts in node features and graph structure. Prior work established that methods trained with standard supervised learning (SL) objectives tend to capture spurious signals from either features and/or structure, leaving the model fragile under distributional changes. To address this, we propose TIDE, a novel and effective Tri-Component Information Decomposition framework that explicitly decomposes information into feature-specific, structure-specific and joint components. TIDE aims to preserve only the label-relevant part of the joint information while filtering out spurio...
| Authors | Danny Wang, Ruihong Qiu, Zi Huang |
|---|---|
| Primary category | cs.LG |
| Published | 13 May 2026 |
| Last updated at source | 14 May 2026 |
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