Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls

• URL: http://arxiv.org/abs/2607.21381v1
• Date: Thu, 23 Jul 2026 14:44:51 GMT
• Title: Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls
• Abstract summary: SeeExplainer is a parameter-free explainer for graph neural networks (GNNs)<n>We first introduce a granular-ball graph refinement mechanism that decomposes a graph into several disjoint granular-balls with no fixed size.<n>We then perturb nodes and edges in the structural graph to generate explanatory subgraphs.
• Score: 30.633193487703142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instance-level explanations aim to reveal the rationale behind a model's decisions for a specific graph. Previous methods explain graph neural networks (GNNs) by selecting important edges to induce subgraphs, where edge importance is assessed by perturbing each edge and observing changes in the model predictions. However, they often neglect the synergistic effects among edges, which are crucial for accurately characterizing edge importance. To address this issue, we propose SeeExplainer, a parameter-free explainer to interpret GNNs. Specifically, we first introduce a granular-ball graph refinement mechanism that decomposes a graph into several disjoint granular-balls with no fixed size, and utilize them as nodes to construct a structural graph. This process can better capture the synergistic effects among edges. Then, we perturb nodes and edges in the structural graph to generate explanatory subgraphs based on their respective contributions. Experiments on several graph classification datasets of different networks show that SeeExplainer outperforms state-of-the-art baselines.

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