SIG: Efficient Self-Interpretable Graph Neural Network for
Continuous-time Dynamic Graphs
SIG: Efficient Self-Interpretable Graph Neural Network for
Continuous-time Dynamic Graphs
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. …