While dynamic graph neural networks have shown promise in various applications, explaining their predictions on continuous-time dynamic graphs (CTDGs) is difficult. This paper investigates a new research task: self-interpretable GNNs for CTDGs. We aim to predict future links within the dynamic graph while simultaneously providing causal explanations for these predictions. There are two key challenges: (1) capturing the underlying structural and temporal information that remains consistent across both independent and identically distributed (IID) and out-of-distribution (OOD) data, and (2) efficiently generating high-quality link prediction results and explanations. To tackle these challenges, we propose a novel causal inference model, namely the Independent and Confounded Causal Model (ICCM). ICCM is then integrated into a deep learning architecture that considers both effectiveness and efficiency. Extensive experiments demonstrate that our proposed model significantly outperforms existing methods across link prediction accuracy, explanation quality, and robustness to shortcut features. Our code and datasets are anonymously released at https://github.com/2024SIG/SIG.

翻译：尽管动态图神经网络已在多种应用中展现出潜力，但解释其在连续时间动态图上的预测结果仍具挑战。本文研究了一个新的研究任务：面向连续时间动态图的自解释图神经网络。我们的目标是在预测动态图中未来链接的同时，为这些预测提供因果解释。这面临两个关键挑战：(1) 捕捉在独立同分布数据与分布外数据中均保持一致的基础结构与时序信息；(2) 高效生成高质量的链接预测结果及解释。为应对这些挑战，我们提出了一种新颖的因果推理模型，即独立与混杂因果模型。该模型随后被集成到一个兼顾效能与效率的深度学习架构中。大量实验表明，我们提出的模型在链接预测准确率、解释质量以及对捷径特征的鲁棒性方面均显著优于现有方法。我们的代码与数据集已匿名发布于 https://github.com/2024SIG/SIG。