AI RESEARCH
GRAFT: Auditing Graph Neural Networks via Global Feature Attribution
arXiv CS.LG
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ArXi:2605.03377v1 Announce Type: new Graph Neural Networks (GNNs) achieve strong performance on node classification tasks but remain difficult to interpret, particularly with respect to which input features drive their predictions. Existing global GNN explainers operate at the structural level identifying recurring subgraph motifs, but none explain model behaviour globally at the level of input node attributes. We propose GRAFT, a posthoc global explanation framework that identifies class-level feature importance profiles for GNNs.