通过粒状球实现具有协同边效应的忠实图解释
Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls
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中文总结 AI 辅助
研究如何忠实解释图神经网络决策，提出无参数解释器SeeExplainer，通过粒状球图细化机制捕捉边的协同效应，在结构图中扰动节点和边生成解释性子图，实验证明其性能优于现有基线。
中文摘要 AI 辅助
实例级解释旨在揭示模型对特定图决策背后的原理。以往方法通过选择重要边来诱导子图解释图神经网络（GNN），但常忽略边之间的协同效应。为解决此问题，我们提出无参数解释器SeeExplainer。先引入粒状球图细化机制，将图分解为多个无固定大小的不相交粒状球，用作节点构建结构图以捕捉边的协同效应。然后在结构图中扰动节点和边生成解释性子图。在不同网络的图分类数据集上实验表明，SeeExplainer优于现有基线。
英文摘要
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.
发表机构
- Chongqing University of Posts and Telecommunications（重庆邮电大学）
- Chongqing Three Gorges University（重庆三峡学院）
机构由 AI 辅助整理，请以论文原文为准。
