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English(EN) Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution

新方法增强了时序图网络的可解释性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新方法，通过关注时序图网络（TGNs）的记忆模块来解释其预测。该方法利用拓扑归因树来评估邻居及其历史数据的影响，并利用记忆回溯树来量化过去事件如何塑造节点记忆。该方法将LRP应用于TGNs，并包含用于识别重要事件的优化目标，在节点属性预测、链接预测和图分类等各种时序图数据集和任务上，已证明其解释的忠实性并优于现有基线。 AI

影响 这项研究提供了一种理解和信任时序图网络的新方法，有可能改善其在关键领域的应用。

排序理由 该集群包含一篇详细介绍AI模型解释新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新方法增强了时序图网络的可解释性

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该集群包含一篇详细介绍AI模型解释新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yazheng Liu, Xi Zhang, Sihong Xie, Hui Xiong ·

    通过记忆回溯和拓扑归因实现时序图网络的可解释性

    arXiv:2607.07716v1 Announce Type: cross Abstract: Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy. Understanding which historical events drive model predictions can enhance trustworthiness of …

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