English(EN) Efficient Higher-order Subgraph Attribution via Message Passing

新算法高效解释图神经网络的决策

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-22 04:00

研究人员开发了新的算法来高效解释图神经网络（GNN）的决策过程。这些方法基于消息传递技术，显著降低了GNN-LRP等高阶归因方案的计算复杂度。新算法可以在线性时间（相对于网络深度）内归因子图，为理解GNN如何利用特征和邻近图信息提供了一种可扩展且有用的方法。 AI

影响提供了一种更有效的方法来理解GNN的决策过程，可能提高使用图数据的AI系统的可解释性和信任度。

排序理由该集群包含一篇详细介绍图神经网络解释新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍图神经网络解释新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
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Story freshness: 139 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

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arXiv cs.LG TIER_1 English(EN) · Ping Xiong, Thomas Schnake, Gr\'egoire Montavon, Klaus-Robert M\"uller, Shinichi Nakajima · 2026-05-22 04:00

高效高阶子图归因通过消息传递实现

arXiv:2605.22385v1 Announce Type: new Abstract: Explaining graph neural networks (GNNs) has become more and more important recently. Higher-order interpretation schemes, such as GNN-LRP (layer-wise relevance propagation for GNN), emerged as powerful tools for unraveling how diffe…