LLMs as Proposers: Generative Class-Level Counterfactual Explainer for GNNs

Counterfactual explanations for graph neural networks (GNNs) require finding the minimal perturbations on the input graphs for changing GNNs’ predictions. Nevertheless, the instance-level edits fail to provide a holistic view of the model’s global decision boundaries. To address this challenge, we introduce LAPCE (LLM-as-Proposer for Counterfactual Explanation), a post-hoc class-level framework organized into the stages of propose–verify–select, identifying shared, discriminative subgraphs that systematically govern decision boundaries across a whole class. In the proposal stage, an instruction-conditioned chemical large language model (LLM) generates candidate subgraphs for counterfactual explanation, circumventing the combinatorial search in the discrete space. In the verification stage, each candidate subgraph is matched to a molecule, whose residual is further evaluated: The candidate subgraph is valid only if the residual molecule is valid and the GNN’s predicted label changes. In the selection stage, the candidates are selected and ordered using their verification results to form a compact class-level explanation across semantic-cost thresholds and explanation budgets, while controlling redundancy. Empirical results show that LAPCE outperforms the strongest baseline across four molecular tasks.