AI RESEARCH
ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
arXiv CS.AI
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ArXi:2605.19822v1 Announce Type: cross Temporal graph neural networks (TGNNs) have gained significant traction for solving real-world temporal graph tasks. However, their interpretability remains limited, as most TGNNs fail to identify which historical interactions most influence a given prediction. Despite promising progress on interpretable TGNNs, existing methods predominantly focus on previously seen historical interactions, which we term stability patterns, while overlooking newly emerging first-time interactions, which we term transition patterns.