English(EN) Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming

新方法利用稀疏线性规划高效学习平衡符号图

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种新颖的方法，用于高效学习平衡符号图，该图包含数据中的正负相关性。这种新方法通过为图拉普拉斯矩阵的每一列构建一个约束线性规划（LP）问题来扩展基于线性规划的技术。该方法在理论上保证收敛，并在合成和真实世界数据集上展示出优于现有方法的性能。学习到的平衡图能够有效重用最初为正图设计的谱滤波工具和图神经网络。 AI

影响能够更有效地在复杂的符号图数据上使用谱滤波器和GNN。

排序理由该集群包含一篇提交到arXiv的研究论文，详细介绍了一种学习平衡符号图的新计算方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka, Gene Cheung · 2026-09-02 04:00

通过稀疏线性规划高效学习平衡有符号图

arXiv:2506.01826v2 Announce Type: replace Abstract: Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed graph with no cycles containing an odd number of n…

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通过稀疏线性规划高效学习平衡有符号图

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