Graph neural networks (GNNs) are a type of neural model that tackle graphical tasks in an end-to-end manner. Recently, GNNs have been receiving increased attention in machine learning and data mining communities because of the higher performance they achieve in various tasks, including graph classification, link prediction, and recommendation. However, the complicated dynamics of GNNs make it difficult to understand which parts of the graph features contribute more strongly to the predictions. To handle the interpretability issues, recently, various GNN explanation methods have been proposed. In this study, a flexible model agnostic explanation method is proposed to detect significant structures in graphs using the Hilbert-Schmidt independence criterion (HSIC), which captures the nonlinear dependency between two variables through kernels. More specifically, we extend the GraphLIME method for node explanation with a group lasso and a fused lasso-based node explanation method. The group and fused regularization with GraphLIME enables the interpretation of GNNs in substructure units. Then, we show that the proposed approach can be used for the explanation of sequential graph classification tasks. Through experiments, it is demonstrated that our method can identify crucial structures in a target graph in various settings.

翻译：图神经网络（GNNs）是一类以端到端方式处理图任务的神经模型。近年来，由于GNNs在图分类、链接预测和推荐等多种任务中展现出更高的性能，它们在机器学习和数据挖掘领域受到了越来越多的关注。然而，GNNs复杂的动力学机制使得理解图特征的哪些部分对预测贡献更大变得困难。为了解决可解释性问题，近期提出了多种GNN解释方法。本研究提出了一种灵活的模型无关解释方法，利用希尔伯特-施密特独立性准则（HSIC）检测图中的重要结构，该准则通过核函数捕捉两个变量之间的非线性依赖关系。具体来说，我们将用于节点解释的GraphLIME方法扩展为基于组套索和融合套索的节点解释方法。GraphLIME中的组正则化和融合正则化使得能够以子结构单元的方式解释GNNs。随后，我们展示了所提方法可用于序列图分类任务的解释。通过实验证明，我们的方法能够在多种设置下识别目标图中的关键结构。