研究人员开发了两种新的模型无关的时间图神经网络 (TGNN) 解释器，利用 Shapley 和 Owen 值。这些方法旨在提高结合了空间和时间数据的 TGNN 预测的可解释性。与各种数据集上的现有最先进方法相比，这些解释器表现出更优越的性能，并揭示了常见 TGAT 实现中有关时间戳提取的一个缺陷。 AI
影响 增强了 TGNN 的可解释性，可能有助于改进模型调试和可信度。
排序理由 关于时间图神经网络可解释性方法的学术论文。
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arXiv:2604.24078v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) have become increasingly popular in recent years due to their superior predictive performance by combining both spatial and temporal information. However, how these models utilize the informati…
Temporal Graph Neural Networks (TGNNs) have become increasingly popular in recent years due to their superior predictive performance by combining both spatial and temporal information. However, how these models utilize the information to make predictions is rather unexplored, lea…