Graphs neural networks (GNNs) learn node features by aggregating and
combining neighbor information, which have achieved promising performance on
many graph tasks. However, GNNs are mostly treated as black-boxes and lack
human intelligible explanations. Thus, they cannot be fully trusted and used in
certain application domains if GNN models cannot be explained. In this work, we
propose a novel approach, known as XGNN, to interpret GNNs at the model-level.
Our approach can provide high-level insights and generic understanding of how
GNNs work. In particular, we propose to explain GNNs by training a graph
generator so that the generated graph patterns maximize a certain prediction of
the model.We formulate the graph generation as a reinforcement learning task,
where for each step, the graph generator predicts how to add an edge into the
current graph. The graph generator is trained via a policy gradient method
based on information from the trained GNNs. In addition, we incorporate several
graph rules to encourage the generated graphs to be valid. Experimental results
on both synthetic and real-world datasets show that our proposed methods help
understand and verify the trained GNNs. Furthermore, our experimental results
indicate that the generated graphs can provide guidance on how to improve the
trained GNNs.

提出了一种名为 XGNN 的新方法，通过训练图生成器来解释 GNN 在模型级别的工作，其中将图形生成作为强化学习任务，并利用训练的 GNN 信息对图形生成器进行策略梯度方法的训练，最终可以有效地理解和验证所训练的 GNN 的结果，并在生成的图形上改进 GNN 的性能。