English(EN) RAW-Explainer: Post-hoc Explanations of Path Reasoning Graph Neural Networks for Knowledge Graph Completion

新方法RAW-Explainer增强了用于知识图谱的GNN的可解释性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员开发了RAW-Explainer，一种用于解释知识图谱补全中使用的路径推理图神经网络（GNN）预测的新颖方法。该技术为链接预测任务生成简洁且连接的子图解释，弥补了当前GNN可解释性方法通常侧重于节点或图级别任务的不足。RAW-Explainer利用异构信息进行事实解释，并采用随机游走目标来确保连通性，同时还引入了一个鲁棒的评估器来处理评估较小解释性子图时出现的分布变化。 AI

影响增强了图神经网络的可解释性，可能提高知识图谱应用中的信任度和采用率。

排序理由该集群包含一篇详细介绍GNN可解释性新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Ryoji Kubo, Samyam Lamichhane, Djellel Difallah · 2026-10-19 04:00

RAW-Explainer：用于知识图谱补全的路径推理图神经网络的后验解释

arXiv:2506.12558v2 Announce Type: replace Abstract: Path reasoning graph neural networks have demonstrated state-of-the-art performance on knowledge graph completion tasks such as link prediction. However, interpreting their predictions remains a challenging open problem, as most…

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报道来源 [1]

RAW-Explainer：用于知识图谱补全的路径推理图神经网络的后验解释

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