  Temporal Graph Neural Networks (TGNN) have the ability to capture both the
graph topology and dynamic dependencies of interactions within a graph over
time. There has been a growing need to explain the predictions of TGNN models
due to the difficulty in identifying how past events influence their
predictions. Since the explanation model for a static graph cannot be readily
applied to temporal graphs due to its inability to capture temporal
dependencies, recent studies proposed explanation models for temporal graphs.
However, existing explanation models for temporal graphs rely on post-hoc
explanations, requiring separate models for prediction and explanation, which
is limited in two aspects: efficiency and accuracy of explanation. In this
work, we propose a novel built-in explanation framework for temporal graphs,
called Self-Explainable Temporal Graph Networks based on Graph Information
Bottleneck (TGIB). TGIB provides explanations for event occurrences by
introducing stochasticity in each temporal event based on the Information
Bottleneck theory. Experimental results demonstrate the superiority of TGIB in
terms of both the link prediction performance and explainability compared to
state-of-the-art methods. This is the first work that simultaneously performs
prediction and explanation for temporal graphs in an end-to-end manner.
