结构平衡的验证与预测:数据驱动的视角

Lulu Pan, Haibin Shao, M. Mesbahi
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引用次数: 6

摘要

结构平衡在具有吸引和排斥相互作用的网络系统中起着重要的作用,这种相互作用可以用符号网络来表征。本文证明了可由有符号拉普拉斯矩阵的特征向量来推断有符号网络中相互作用类型的集合。此外,还证明了由因子图的图笛卡尔积导出的图是结构平衡的,当且仅当其因子是结构平衡的。根据理论结果,从数据驱动的角度,利用动态模态分解对签名网络的结构平衡进行验证和预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Verification and prediction of structural balance: A data-driven perspective
The structural balance plays a fundamental role in networked systems with both attractive and repulsive interactions, which can be characterized by a signed network. In this paper, we show that the ensemble of type of interaction in a signed network can be inferred from the eigenvector of the signed Laplacian matrix. Also, it has been shown that a graph, derived from the graph Cartesian product of factor graphs, is structurally balanced if and only if its factors are structurally balanced. According to the theoretical results, the verification and prediction of the structural balance of the signed network is presented from a data-driven perspective by utilizing the dynamic mode decomposition.
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