Graph Neural Networks (GNNs) are effective for node classification in graph-structured data, but they lack explainability, especially at the global level. Current research mainly utilizes subgraphs of the input as local explanations or generates new graphs as global explanations. However, these graph-based methods are limited in their ability to explain classes with multiple sufficient explanations. To provide more expressive explanations, we propose utilizing class expressions (CEs) from the field of description logic (DL). Our approach explains heterogeneous graphs with different types of nodes using CEs in the EL description logic. To identify the best explanation among multiple candidate explanations, we employ and compare two different scoring functions: (1) For a given CE, we construct multiple graphs, have the GNN make a prediction for each graph, and aggregate the predicted scores. (2) We score the CE in terms of fidelity, i.e., we compare the predictions of the GNN to the predictions by the CE on a separate validation set. Instead of subgraph-based explanations, we offer CE-based explanations.

翻译：图神经网络（GNN）在图结构数据的节点分类任务中效果显著，但其可解释性不足，尤其在全局层面。现有研究主要利用输入的子图作为局部解释，或生成新图作为全局解释。然而，这些基于图的方法在解释具有多个充分解释的类别时存在局限。为提供更具表达力的解释，我们提出利用描述逻辑（DL）领域中的类表达式（CE）。本方法采用EL描述逻辑中的CE，对包含不同类型节点的异构图进行解释。为从多个候选解释中识别最佳解释，我们采用并比较两种不同的评分函数：(1) 对给定CE，构建多个图，让GNN对每个图进行预测，并聚合预测得分；(2) 依据保真度对CE评分，即在独立验证集上比较GNN预测与CE预测的一致性。我们提供基于CE的解释，而非基于子图的解释。