Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}" Existing explanation methods focus on the supervised settings, \eg, node classification and graph classification, while the explanation for unsupervised graph-level representation learning is still unexplored. The opaqueness of the graph representations may lead to unexpected risks when deployed for high-stake decision-making scenarios. In this paper, we advance the Information Bottleneck principle (IB) to tackle the proposed explanation problem for unsupervised graph representations, which leads to a novel principle, \textit{Unsupervised Subgraph Information Bottleneck} (USIB). We also theoretically analyze the connection between graph representations and explanatory subgraphs on the label space, which reveals that the expressiveness and robustness of representations benefit the fidelity of explanatory subgraphs. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our developed explainer and the validity of our theoretical analysis.
翻译:由于图形神经网络(GNN)在不同领域表现优异,人们越来越关注GNN的解释问题“输入图的哪一部分是决定模型决定的最关键部分? ” 。“现有解释方法侧重于受监督的设置、\eg、节点分类和图形分类,而未受监督的图形级代表性学习的解释仍未得到探讨。图形表达方式的不透明性可能会导致在为高级决策情景部署时出现出乎意料的风险。在本文中,我们推进了信息瓶颈原则(IB),以解决未受监督的图形表达方式的拟议解释问题,这导致了一种新的原则,即:\textit{未受监督的子图层信息布尔内克}(USIB)。我们还从理论上分析了标签空间的图形表达方式和解释性子图之间的联系,这表明,在应用高清晰度和稳健度的表达方式有利于解释性子图的准确性。在合成和现实世界数据集上的实验结果显示了我们所发展的解释的优越性以及我们理论分析的正确性。