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.

GNNs 利用节点表示学习构建的强大模型在与图相关的许多机器学习问题中表现出色，但其部署的主要障碍在于社会可接受性和可信度问题，我们提出了通过挖掘隐藏层的激活规则来理解 GNN 对世界的感知，以解决这一问题，并介绍了主观激活模式领域。通过信息论的基于背景知识的方法来确定激活规则的兴趣度，从而揭示了 GNN 用于分类图的特征和不同层所构建的隐藏特征，同时这些规则还可用于解释 GNN 的决策。实验结果表明在合成和真实数据集上，这些规则在解释图分类方面具有高竞争性，可提高 200% 的准确度。