arXiv: cs.AI preprintsInternational6 November 2026
Value or Attention: Investigating Layer-wise Relevance Propagation for Graph Attention Networks
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arXiv:2611.08088v1 Announce Type: cross Abstract: Graph Attention Networks (GATs) are increasingly used in domains where scientists need to read a model's reasoning, from medicine to materials science, yet the routing they learn remains opaque to most explainers. Layer-wise Relevance Propagation (LRP) follows the model's computation, but in an attention layer the relevance reaching a node is the sum of two mechanically different quantities: its content relevance through the value path and its routing relevance through the attention path. Existing rules either drop one or add them into a single
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