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English(EN) Explaining Temporal Graph Neural Networks via Feature-induced Information Flow

新方法增强了时序图神经网络的可解释性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新方法来解释基于事件的时序图神经网络（ETGNNs）的工作原理。当前的方法仅分析信息流的一部分，忽略了通过捕获长期时间依赖性的事件诱导变量的关键路径。所提出的方法基于归一化相关性度量（NRM）框架，分析了包括事件嵌入和事件诱导变量在内的整个信息流，以提供更全面和可解释的解释。该方法已在合成和真实数据集上进行了评估，证明其性能优于现有技术。 AI

影响 增强了复杂时序图神经网络的可解释性，有助于社交网络分析和流行病追踪等应用的调试和信任。

排序理由 该集群包含一篇详细介绍新AI模型解释方法的论文。

在 arXiv cs.LG 阅读 →

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新方法增强了时序图神经网络的可解释性

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ping Xiong, Thomas Schnake, Klaus-Robert M\"uller, Shinichi Nakajima ·

    通过特征诱导信息流解释时序图神经网络

    arXiv:2606.27201v1 Announce Type: new Abstract: 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.…

  2. arXiv cs.LG TIER_1 English(EN) · Shinichi Nakajima ·

    通过特征诱导信息流解释时序图神经网络

    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 sign…

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