  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.
