An Energy Function for Computing Structural Balance in Fully Signed Network

He, Xiaochen; Du, Haifeng; Xu, Xiangning; Wei, Du

doi:10.1109/tcss.2020.2973215

Cited by 8 publications

(19 citation statements)

References 47 publications

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“…To some extent crossover represents long-term search, while mutation represents short-term search. Thus appropriate setting of P m = 1−P c enables a balance to be found between long-term and short-term search, which helps to increase the efficiency of the genetic algorithm [64,65].…”

Section: Plos Onementioning

confidence: 99%

“…Local search is effective in reducing inefficient exploration and not only improves the accuracy but also speeds up the convergence [64][65][66]. Here we employ a hill-climbing technique presented as Algorithm 2.…”

Section: Local Searchmentioning

confidence: 99%

“…WH companies investigated by the New Urbanization and Sustainable Development Group of Xi'an Jiaotong University [65]. The three networks were constructed from a single-item…”

Section: Plos Onementioning

confidence: 99%

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A memetic algorithm for finding multiple subgraphs that optimally cover an input network

Wang²,

Du³

et al. 2023

PLoS ONE

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Finding dense subgraphs is a central problem in graph mining, with a variety of real-world application domains including biological analysis, financial market evaluation, and sociological surveys. While a series of studies have been devoted to finding subgraphs with maximum density, the problem of finding multiple subgraphs that best cover an input network has not been systematically explored. The present study discusses a variant of the densest subgraph problem and presents a mathematical model for optimizing the total coverage of an input network by extracting multiple subgraphs. A memetic algorithm that maximizes coverage is proposed and shown to be both effective and efficient. The method is applied to real-world networks. The empirical meaning of the optimal sampling method is discussed.

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Section: Plos Onementioning

confidence: 99%

Section: Local Searchmentioning

confidence: 99%

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A memetic algorithm for finding multiple subgraphs that optimally cover an input network

Wang²,

Du³

et al. 2023

PLoS ONE

Self Cite

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show abstract

“…Du et al [50] introduced the notion of the node attribute into the signed network and referred to a network that contained both node and edge signs as a fully signed network. He et al [51] analyzed the balance status of the Chinese rural system using data on migrant workers' civil rights awareness (node attribute) and their information-difusion networks (edge attribute). Studies of fully signed networks considering both node and edge attributes enrich traditional empirical research methods and broaden the application of network science in social science.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Modularity for Computing Community Structure in Fully Signed Networks

Zhang

Zhu

2023

Complexity

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The community structure in fully signed networks that considers both node attributes and edge signs is important in computational social science; however, its physical description still requires further exploration, and the corresponding measurement remains lacking. In this paper, we present a generalized framework of community structure in fully signed networks, based on which a variant of modularity is designed. An optimization algorithm that maximizes modularity to detect potential communities is also proposed. Experiments show that the proposed method can efficiently optimize the objective function and perform effective community detection.

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“…Across these contexts, it is often of interest to identify clusters of nodes that are internally cohesive and mutually divisive, and thus partially satisfy the conditions of generalized balance 8 – 11 . Recent computational work on signed network analysis has focused on determining the network’s level of balance in general 12 – 15 , and in the context of signed graphs with node attributes 16 , 17 . However, although optimization-based methods exist for estimating a network’s level of balance 18 by heuristically partitioning it into clusters 13 or computing its exact level of balance by optimally partitioning it into clusters 2 , 19 , 20 , identifying an optimal partition of nodes into clusters that corresponds to the network’s level of k - balance (a.k.a.…”

Section: Introductionmentioning

confidence: 99%

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

Aref

Neal

2021

Sci Rep

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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.

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An Energy Function for Computing Structural Balance in Fully Signed Network

Cited by 8 publications

References 47 publications

A memetic algorithm for finding multiple subgraphs that optimally cover an input network

A memetic algorithm for finding multiple subgraphs that optimally cover an input network

A Generalized Modularity for Computing Community Structure in Fully Signed Networks

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

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