When explaining temporal graph models, you need to track information flowing through event-induced variables—not just embeddings—to capture how long-range dependencies actually work in the network.
This paper develops a new method to explain how Temporal Graph Neural Networks make predictions by tracking information flow through all components, not just embeddings. The approach uses a framework called Normalized Relevance Measure to systematically decompose complex temporal graph models and identify which events and interactions matter most for predictions.