English(EN) Stability Analysis of Signed Graphs under Structural Perturbations

新研究分析扰动下带符号图学习的稳定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

本文研究了带符号图学习方法在面对结构变化（如边的添加、删除或符号翻转）时的稳定性。该研究引入了一个理论框架，用于分析这些扰动如何与节点特征相关性结合，影响学习表示的稳定性。基于此分析，提出了一种新颖的扰动策略，用于识别有影响力的带符号边修改，这些修改在应用后会持续降低下游链接预测的性能。 AI

影响这项研究有助于加深对带符号图学习的理论理解，有望为网络分析带来更鲁棒的模型。

排序理由该集群包含一篇关于带符号图学习的理论分析和方法的学术论文。[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: 4 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇关于带符号图学习的理论分析和方法的学术论文。[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.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

arXiv cs.LG TIER_1 English(EN) · Ning Zhang, Li Zhang · 2026-10-23 04:00

Stability Analysis of Signed Graphs under Structural Perturbations

arXiv:2610.30444v1 Announce Type: new Abstract: Signed graph learning has emerged as a powerful framework for modeling networks with both positive and negative interactions. While graph-filter-based methods have achieved promising empirical performance on signed networks, their s…