Explaining Temporal Graph Neural Networks via Feature-induced Information Flow
Event-based Temporal Graph Neural Networks (ETGNNs) have demonstrated strong performance across a wide range of applications, including social network analysis, epidemic tracing, recommender systems, and political event forecasting. However, their increasing complexity poses significant challenges for explainability. Existing explanation methods focus only on a subset of the information flow within ETGNNs, typically tracing contributions from the event-related embeddings to the output. Consequently, they overlook the important pathways through event-induced variables, which mediate interactions between nodes and thereby play a central role in capturing long-range temporal dependencies. To...
| Authors | Ping Xiong, Thomas Schnake, Klaus-Robert Müller et al. |
|---|---|
| Primary category | cs.LG |
| Published | 25 June 2026 |
| Last updated at source | 14 July 2026 |
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