用图社区分析提升GNN分类可解释性，精准定位关键结构
The GECo algorithm for Graph Neural Networks Explanation
- 基于图社区密度特性设计解释方法，利用消息传递机制增强可解释性
- 在6个合成数据集和4个真实数据集上优于主流方法，尤其在人工数据上表现突出
- 适合需要理解GNN决策依据的科研与高风险应用场景
图神经网络（GNN）能有效处理复杂数据及其连接关系，但其缺乏可解释性限制了在敏感领域的应用。本文提出一种新方法GECo，通过图社区（graph communities）提升图分类任务的可解释性。由于社区是节点高度连接的子集，该特性应在图分类中起作用，尤其契合GNN的消息传递机制。GECo分析各社区对分类结果的贡献，构建掩码以突出图中相关结构。在六个人工图数据集和四个真实世界图数据集上测试，对比PGMExplainer、PGExplainer、GNNExplainer和SubgraphX等主流方法，使用四种评估指标。结果显示，GECo在人工数据集上全面领先，在多数真实数据集上也表现更优。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) are powerful models that can manage complex data sources and their interconnection links. One of GNNs' main drawbacks is their lack of interpretability, which limits their application in sensitive fields. In this paper, we introduce a new methodology involving graph communities to address the interpretability of graph classification problems. The proposed method, called GECo, exploits the idea that if a community is a subset of graph nodes densely connected, this property should play a role in graph classification. This is reasonable, especially if we consider the message-passing mechanism, which is the basic mechanism of GNNs. GECo analyzes the contribution to the classification result of the communities in the graph, building a mask that highlights graph-relevant structures. GECo is tested for Graph Convolutional Networks on six artificial and four real-world graph datasets and is compared to the main explainability methods such as PGMExplainer, PGExplainer, GNNExplainer, and SubgraphX using four different metrics. The obtained results outperform the other methods for artificial graph datasets and most real-world datasets.
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