Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks
Manuel Guerrero,
Consolación Gil,
Francisco G. Montoya,
Alfredo Alcayde and
Raúl Baños
Additional contact information
Manuel Guerrero: CeiA3, Department of Informatics, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain
Consolación Gil: CeiA3, Department of Informatics, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain
Francisco G. Montoya: CeiA3, Department of Engineering, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain
Alfredo Alcayde: CeiA3, Department of Engineering, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain
Raúl Baños: CeiA3, Department of Engineering, University of Almería, Carretera de Sacramento s/n, 04120 Almería, Spain
Mathematics, 2020, vol. 8, issue 11, 1-18
Abstract:
Real-world complex systems are often modeled by networks such that the elements are represented by vertices and their interactions are represented by edges. An important characteristic of these networks is that they contain clusters of vertices densely linked amongst themselves and more sparsely connected to nodes outside the cluster. Community detection in networks has become an emerging area of investigation in recent years, but most papers aim to solve single-objective formulations, often focused on optimizing structural metrics, including the modularity measure. However, several studies have highlighted that considering modularity as a unique objective often involves resolution limit and imbalance inconveniences. This paper opens a new avenue of research in the study of multi-objective variants of the classical community detection problem by applying multi-objective evolutionary algorithms that simultaneously optimize different objectives. In particular, they analyzed two multi-objective variants involving not only modularity but also the conductance metric and the imbalance in the number of nodes of the communities. With this aim, a new Pareto-based multi-objective evolutionary algorithm is presented that includes advanced initialization strategies and search operators. The results obtained when solving large-scale networks representing real-life power systems show the good performance of these methods and demonstrate that it is possible to obtain a balanced number of nodes in the clusters formed while also having high modularity and conductance values.
Keywords: network optimization; community detection; modularity; imbalance; conductance; multi-objective evolutionary algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:11:p:2048-:d:446364
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