A controllable synthetic benchmark on contextual SBM graphs reveals distance-misaligned training in Graph Transformers, with an oracle adaptive controller improving performance by matching task-specific distance targets.
Demystifying oversmoothing in attention-based graph neural networks
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Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control
A controllable synthetic benchmark on contextual SBM graphs reveals distance-misaligned training in Graph Transformers, with an oracle adaptive controller improving performance by matching task-specific distance targets.