None Relevant Walk Search for Explaining Graph Neural Networks

新算法通过降低游走搜索复杂度来提高GNN的可解释性

作者 PulseAugur 编辑部 · [2 sources] · 2026-05-22 14:21

研究人员开发了新的多项式时间算法，以解决识别图神经网络（GNN）中相关游走所面临的指数级计算复杂度问题。这一进展显著提高了GNN-LRP的适用性，GNN-LRP是一种通过分析游走中的信息流来解释GNN的方法。所提出的基于最大乘积方法（max-product method）的算法，能够精确和近似地识别出排名前K的相关游走，并在包括流行病学、分子和自然语言任务在内的各种基准测试中证明了其有效性。 AI

影响增强了GNN的可解释性和可信度，可能增加其在安全关键应用中的采用率。

排序理由该集群包含一篇详细介绍用于提高图神经网络可解释性新算法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.LG TIER_1 · Ping Xiong, Thomas Schnake, Michael Gastegger, Gr\'egoire Montavon, Klaus-Robert M\"uller, Shinichi Nakajima · 2026-05-25 04:00

Relevant Walk Search for Explaining Graph Neural Networks

arXiv:2605.23673v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, and robustness. Layer-wise relevance propagation for GNNs (GNN-LRP) evaluates the …
arXiv cs.LG TIER_1 · Shinichi Nakajima · 2026-05-22 14:21

Relevant Walk Search for Explaining Graph Neural Networks

Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, and robustness. Layer-wise relevance propagation for GNNs (GNN-LRP) evaluates the relevance of \emph{walks} to reveal important in…

报道来源 [2]

Relevant Walk Search for Explaining Graph Neural Networks

Relevant Walk Search for Explaining Graph Neural Networks

相关实体

相关话题