Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control
Graph Transformers can mix information globally, but this flexibility also creates failure modes: some tasks require long-range communication while others are better served by local interaction. We study this through a synthetic node-classification benchmark on contextual stochastic block model graphs, where labels are generated by a controllable mixture of local and far-shell signals. We define distance-misaligned training as a mismatch between where label-relevant information lies and where the model allocates communication over graph distance. On this benchmark, we find three points. First, the preferred graph-distance bias changes systematically with task locality. Second, an oracle a...
| Authors | Qinhan Hou, Jing Tang |
|---|---|
| Primary category | cs.LG |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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