Revealing Combinatorial Reasoning of GNNs via Graph Concept Bottleneck Layer
Despite their success in various domains, the growing dependence on GNNs raises a critical concern about the nature of the combinatorial reasoning underlying their predictions, which is often hidden within their black-box architectures. Addressing this challenge requires understanding how GNNs translate topological patterns into logical rules. However, current works only uncover the hard logical rules over graph concepts, which cannot quantify the contribution of each concept to prediction. Moreover, they are post-hoc interpretable methods that generate explanations after model training and may not accurately reflect the true combinatorial reasoning of GNNs, since they approximate it with...
| Authors | Yue Niu, Zhaokai Sun, Jiayi Yang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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