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: this https URL

提出了一种可解释的空时图神经网络（STExplainer）框架，以提高 Spatio-temporal Graph Neural Networks（STGNNs）的可解释性，同时实现准确的预测和可信的解释。通过与其他模型的实验比较，证明 STExplainer 在交通和犯罪预测任务中具有更高的预测精度和可解释度，并能更好地处理数据丢失和稀疏性问题。