Spatio-temporal graph neural networks (STGNNs) have gained popularity as a powerful tool for effectively modeling spatio-temporal dependencies in diverse real-world urban applications, including intelligent transportation and public safety. However, the black-box nature of STGNNs limits their interpretability, hindering their application in scenarios related to urban resource allocation and policy formulation. To bridge this gap, we propose an Explainable Spatio-Temporal Graph Neural Networks (STExplainer) framework that enhances STGNNs with inherent explainability, enabling them to provide accurate predictions and faithful explanations simultaneously. Our framework integrates a unified spatio-temporal graph attention network with a positional information fusion layer as the STG encoder and decoder, respectively. Furthermore, we propose a structure distillation approach based on the Graph Information Bottleneck (GIB) principle with an explainable objective, which is instantiated by the STG encoder and decoder. Through extensive experiments, we demonstrate that our STExplainer outperforms state-of-the-art baselines in terms of predictive accuracy and explainability metrics (i.e., sparsity and fidelity) on traffic and crime prediction tasks. Furthermore, our model exhibits superior representation ability in alleviating data missing and sparsity issues. The implementation code is available at: https://github.com/HKUDS/STExplainer.

翻译：时空图神经网络（STGNNs）已成为一种强大工具，可有效建模智能交通和公共安全等多样化现实城市应用中的时空依赖性。然而，STGNNs的黑箱特性限制了其可解释性，阻碍了其在城市资源配置与政策制定等场景中的应用。为弥补这一不足，我们提出可解释的时空图神经网络框架（STExplainer），通过增强STGNNs的内在可解释性，使其同时提供准确预测与可信解释。该框架将统一的时空图注意力网络与位置信息融合层分别作为时空图编码器和解码器。此外，我们基于图信息瓶颈（GIB）原理提出一种结构蒸馏方法，该方法由可解释性目标驱动，并通过时空图编码器与解码器实例化。大量实验表明，在交通与犯罪预测任务中，我们的STExplainer在预测准确性和可解释性指标（即稀疏性与保真度）上均优于最先进基线模型。进一步地，该模型在缓解数据缺失与稀疏性问题方面展现出优越的表征能力。实现代码已开源：https://github.com/HKUDS/STExplainer。