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 this https URL

针对连续时间动态图（CTDGs）上的预测困难，本文研究了自解释的动态图神经网络（GNNs）。我们旨在在动态图中预测未来的链接并同时提供因果解释。通过提出独立和混淆因果模型（ICCM）并将其整合到同时考虑效果和效率的深度学习结构中，我们解决了捕捉基础结构和时间信息的不一致性和分布外数据的挑战，以及高质量链接预测结果和解释的高效生成。大量实验证明，我们的模型在链接预测准确性、解释质量和对快捷特征的鲁棒性上明显优于现有方法。我们的代码和数据集已匿名发布在此 URL。