GNNs are powerful models based on node representation learning that perform particularly well in many machine learning problems related to graphs. The major obstacle to the deployment of GNNs is mostly a problem of societal acceptability and trustworthiness, properties which require making explicit the internal functioning of such models. Here, we propose to mine activation rules in the hidden layers to understand how the GNNs perceive the world. The problem is not to discover activation rules that are individually highly discriminating for an output of the model. Instead, the challenge is to provide a small set of rules that cover all input graphs. To this end, we introduce the subjective activation pattern domain. We define an effective and principled algorithm to enumerate activations rules in each hidden layer. The proposed approach for quantifying the interest of these rules is rooted in information theory and is able to account for background knowledge on the input graph data. The activation rules can then be redescribed thanks to pattern languages involving interpretable features. We show that the activation rules provide insights on the characteristics used by the GNN to classify the graphs. Especially, this allows to identify the hidden features built by the GNN through its different layers. Also, these rules can subsequently be used for explaining GNN decisions. Experiments on both synthetic and real-life datasets show highly competitive performance, with up to 200% improvement in fidelity on explaining graph classification over the SOTA methods.

翻译：图神经网络（GNNs）是基于节点表示学习的强大模型，在与图相关的诸多机器学习问题中表现尤为出色。GNNs部署的主要障碍很大程度上源于社会接受度与可信度问题，这些特性要求明确揭示此类模型的内部工作机制。本文提出通过挖掘隐藏层中的激活规则来理解GNNs如何感知世界。该问题的核心并非发现对模型输出具有高度区分性的独立激活规则，而在于提供能够覆盖所有输入图的精简规则集。为此，我们引入了主观激活模式域，并设计了一种高效且原理清晰的算法来枚举各隐藏层的激活规则。所提出的规则兴趣度量化方法植根于信息论，能够兼顾输入图数据的背景知识。这些激活规则可通过包含可解释特征的模式语言进行重描述。实验表明，激活规则能够揭示GNN用于图分类的特征特性，尤其有助于识别GNN在不同层级构建的隐藏特征。同时，这些规则可后续用于解释GNN的决策过程。在合成数据集和真实数据集上的实验显示出极具竞争力的性能，在图分类解释的保真度指标上较现有最优方法提升最高达200%。