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#machine learning Preprint Sep 2026

RACE: Relation-Level Counterfactual Explanations for Heterogeneous Graph Neural Networks

Counterfactual explanations of graph neural networks identify edge deletions that flip a prediction. On heterogeneous graphs, however, existing methods first collapse the graph into untyped edges, so they cannot answer the question a domain expert actually asks: which relation type drives this prediction? We present RA...

Yu-Xi Yao, Zi-Jun Zhao · 0 citations
#machine learning Preprint Sep 2026

GraphVQ: Structure-Aware Autoregressive Decoding over Context-Quantized Graph Tokens

Graph foundation models need a discrete token representation, but casting a graph as a generatable token sequence faces a structural obstacle: edges spanning beyond the serialization window cannot be emitted in one pass--so one-pass autoregressive generators systematically under-produce cycles--and a single global cond...

Yu-Xi Yao, Zi-Jun Zhao · 0 citations

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