New PASC Framework Enhances Link Sign Prediction in Signed Networks

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一个名为极性不对称结构校准（PASC）的新框架，以改进有符号网络中的链接符号预测。该方法旨在解决严重符号不平衡带来的挑战，在这种不平衡中，少数类关系的错误难以检测。PASC构建了一个仅结构的事前表示，并利用它来校准符号注意力聚合、融合和优化，从而在真实世界数据集上提高了性能。 AI

影响这项研究可能导致有符号网络中更准确的预测，并可能影响那些依赖于分析具有正极性或负极性关系的领域的预测。

排序理由该集群包含一篇详细介绍链接符号预测新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Qiqi Gao, Wenzhuo Song, Xueyan Liu · 2026-09-10 04:00

面向链接符号预测的极性不对称结构校准

arXiv:2609.05896v1 Announce Type: cross Abstract: Link sign prediction (LSP) aims to infer the positive or negative polarity of unobserved links in signed networks. Signed Graph Neural Networks (SGNNs) usually rely on signed-graph structural priors, including structural balance a…