Generating explanations for graph neural networks (GNNs) has been studied to understand their behavior in analytical tasks such as graph classification. Existing approaches aim to understand the overall results of GNNs rather than providing explanations for specific class labels of interest, and may return explanation structures that are hard to access, nor directly queryable.We propose GVEX, a novel paradigm that generates Graph Views for EXplanation. (1) We design a two-tier explanation structure called explanation views. An explanation view consists of a set of graph patterns and a set of induced explanation subgraphs. Given a database G of multiple graphs and a specific class label l assigned by a GNN-based classifier M, it concisely describes the fraction of G that best explains why l is assigned by M. (2) We propose quality measures and formulate an optimization problem to compute optimal explanation views for GNN explanation. We show that the problem is $\Sigma^2_P$-hard. (3) We present two algorithms. The first one follows an explain-and-summarize strategy that first generates high-quality explanation subgraphs which best explain GNNs in terms of feature influence maximization, and then performs a summarization step to generate patterns. We show that this strategy provides an approximation ratio of 1/2. Our second algorithm performs a single-pass to an input node stream in batches to incrementally maintain explanation views, having an anytime quality guarantee of 1/4 approximation. Using real-world benchmark data, we experimentally demonstrate the effectiveness, efficiency, and scalability of GVEX. Through case studies, we showcase the practical applications of GVEX.

翻译：图神经网络（GNN）在分析任务（如图分类）中的行为解释问题已得到广泛研究。现有方法旨在理解GNN的整体结果，而非针对特定目标类别标签提供解释，且可能返回难以访问或无法直接查询的解释结构。我们提出GVEX，一种生成图视图进行解释的新型范式。（1）我们设计了一种双层解释结构，称为解释视图。一个解释视图由一组图模式和一组诱导解释子图构成。给定多图数据库G和基于GNN的分类器M分配的特定类别标签l，该视图简要描述了G中最能解释M为何赋予标签l的部分。（2）我们提出质量度量指标，并构建一个优化问题来计算GNN解释的最优解释视图，证明该问题为$\Sigma^2_P$-难问题。（3）我们提出两种算法。第一种采用"解释-归纳"策略：首先生成基于特征影响最大化且最能解释GNN的高质量解释子图，然后执行归纳步骤生成模式。我们证明该策略可实现1/2的近似比。第二种算法通过批量处理输入节点流，以增量方式维护解释视图，并具有1/4近似比的随时质量保证。通过真实世界基准数据实验，我们验证了GVEX的有效性、效率与可扩展性。案例研究进一步展示了GVEX的实际应用价值。