Graphs neural networks (GNNs) have emerged as a powerful graph learning model due to their superior capacity in capturing critical graph patterns. To gain insights about the model mechanism for interpretable graph learning, previous efforts focus on post-hoc local interpretation by extracting the data pattern that a pre-trained GNN model uses to make an individual prediction. However, recent works show that post-hoc methods are highly sensitive to model initialization and local interpretation can only explain the model prediction specific to a particular instance. In this work, we address these limitations by answering an important question that is not yet studied: how to provide global interpretation of the model training procedure? We formulate this problem as in-process global interpretation, which targets on distilling high-level and human-intelligible patterns that dominate the training procedure of GNNs. We further propose Graph Distribution Matching (GDM) to synthesize interpretive graphs by matching the distribution of the original and interpretive graphs in the feature space of the GNN as its training proceeds. These few interpretive graphs demonstrate the most informative patterns the model captures during training. Extensive experiments on graph classification datasets demonstrate multiple advantages of the proposed method, including high explanation accuracy, time efficiency and the ability to reveal class-relevant structure.

本文提出了一种基于图分布匹配（Graph Distribution Matching）的图神经网络（Graph Neural Networks）全局解释方法，能够高效准确地提取GNN模型训练过程中最具代表性和人类可理解的图形式式，实现了对于图分类问题的高解释准确性和时间效率。

基于分布匹配的图学习全局解释过程