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arXiv 1906.01696cs.SImath.OCphysics.soc-ph
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通过最优划分政治合作的签名网络来检测联盟

Detecting coalitions by optimally partitioning signed networks of political collaboration

Samin Aref, Zachary Neal

备注：
21 pages, 10 figures, 4 tables (including supplementary information) Old title: Legislative effectiveness hangs in the balance: Studying balance and polarization through partitioning signed networks
发表信息：
AREF, S., and NEAL, Z. Detecting coalitions by optimally partitioning signed networks of political collaboration. Scientific Reports, (2020), www.doi.org/10.1038/s41598-020-58471-z
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摘要：

我们提出了新的数学规划模型的最佳分区的一个有符号的图成凝聚力的群体。为了证明这种方法的实用性，我们将其应用于确定自1979年以来在美国国会的联盟，并研究极化联盟的影响，通过法案的有效性。我们的模型产生了一个全局最优的解决方案，以最大限度地减少组内负和组间正边缘的总数的NP-难题。我们通过提供上界和下界来解决密集符号网络的密集计算，然后求解一个优化模型，该模型关闭两个边界之间的差距，并返回顶点的最佳划分。我们的实质性研究结果表明，一个意识形态同质的联盟（即党派极化）的主导地位可以是一个保护性因素，提高立法的有效性。

英文摘要：

We propose new mathematical programming models for optimal partitioning of a signed graph into cohesive groups. To demonstrate the approach's utility, we apply it to identify coalitions in US Congress since 1979 and examine the impact of polarized coalitions on the effectiveness of passing bills. Our models produce a globally optimal solution to the NP-hard problem of minimizing the total number of intra-group negative and inter-group positive edges. We tackle the intensive computations of dense signed networks by providing upper and lower bounds, then solving an optimization model which closes the gap between the two bounds and returns the optimal partitioning of vertices. Our substantive findings suggest that the dominance of an ideologically homogeneous coalition (i.e. partisan polarization) can be a protective factor that enhances legislative effectiveness.

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