Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance

Abstract In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, d...

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Autores principales:	Samin Aref, Zachary P. Neal
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/34a0bce2163942f3b3faba2aff7cd3db
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spelling	oai:doaj.org-article:34a0bce2163942f3b3faba2aff7cd3db2021-12-02T18:37:09ZIdentifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance10.1038/s41598-021-98139-w2045-2322https://doaj.org/article/34a0bce2163942f3b3faba2aff7cd3db2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98139-whttps://doaj.org/toc/2045-2322Abstract In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them to partition signed networks of collaboration and opposition in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party.Samin ArefZachary P. NealNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Samin Aref Zachary P. Neal Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance
description	Abstract In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them to partition signed networks of collaboration and opposition in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party.
format	article
author	Samin Aref Zachary P. Neal
author_facet	Samin Aref Zachary P. Neal
author_sort	Samin Aref
title	Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance
title_short	Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance
title_full	Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance
title_fullStr	Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance
title_full_unstemmed	Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance
title_sort	identifying hidden coalitions in the us house of representatives by optimally partitioning signed networks based on generalized balance
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/34a0bce2163942f3b3faba2aff7cd3db
work_keys_str_mv	AT saminaref identifyinghiddencoalitionsintheushouseofrepresentativesbyoptimallypartitioningsignednetworksbasedongeneralizedbalance AT zacharypneal identifyinghiddencoalitionsintheushouseofrepresentativesbyoptimallypartitioningsignednetworksbasedongeneralizedbalance
_version_	1718377789950787584

Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance

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