分离稳定与动态交互模式，让时序图神经网络解释更准确。
ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
- 通过解耦稳定与过渡模式，构建可解释的时序图模型。
- 在多个数据集上实现高预测性能与更真实的解释结果。
- 适合关注时序图可解释性、特别是新出现交互的研究者。
时序图神经网络（TGNNs）在解决现实世界时序图任务中备受关注，但其可解释性仍受限，多数方法无法识别对当前预测影响最大的历史交互。尽管可解释TGNN已有进展，现有方法主要关注已观测的历史交互（称作稳定模式），却忽视了首次出现的新交互（称作过渡模式）。这两类模式对生成真实可信的时序解释均至关重要。为此，我们提出ST-TGExplainer，一种自解释的TGNN，通过解耦时序图中的稳定与过渡模式，实现更忠实的可解释性。该方法基于解耦信息瓶颈目标，学习一个紧凑的解释子图，在保持事件标签预测能力的同时，显式抑制稳定与过渡模式间的标签相关冗余。大量实验表明，ST-TGExplainer在预测性能和解释真实性方面均表现优异。代码已公开于https://github.com/hjchen-hdu/ST-TGExplainer。
原文摘要 · Abstract (English)
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. Both types of patterns are essential for faithful temporal explanations. To address this limitation, we propose ST-TGExplainer, a self-explainable TGNN that disentangles Stability and Transition patterns in temporal graphs for a more faithful Temporal GNN Explainer. Guided by a disentangled information bottleneck objective, ST-TGExplainer learns a compact explanatory subgraph that remains predictive of the event label while explicitly suppressing label-conditioned redundancy between stability and transition patterns. Extensive experiments demonstrate that ST-TGExplainer achieves strong predictive performance and yields more faithful explanations. Code is available at https://github.com/hjchen-hdu/ST-TGExplainer.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。