B-cos GNNs: Faithful Explanations through Dynamic Linearity
We introduce B-cos GNNs, an inherently explainable class of graph neural networks whose predictions decompose exactly into per-node, per-feature contributions via a single input-dependent linear map. B-cos GNNs use linear (sum-based) aggregation and replace non-linear message and update functions with B-cos transforms. This induces meaningful, task-specific weight-input alignment that is directly accessible through the model's dynamic linearity. Instance-level explanations follow from a single forward and backward pass, requiring no auxiliary explainer, modified learning objective, or perturbation procedure. Instantiated as a GIN, our approach trades small losses in predictive accuracy fo...
| Authors | Joschka Groß, Mohammad Shaique Solanki, Verena Wolf |
|---|---|
| Primary category | cs.LG |
| Published | 19 May 2026 |
| Last updated at source | 27 May 2026 |
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