Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks
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 con...
| Main Authors: | , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
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MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/8922 |
| _version_ | 1789406352219570176 |
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| author | Guerrero López, Manuel Alejandro Gil Montoya, Consolación Gil Montoya, Francisco Alcayde García, Alfredo Baños Navarro, Raúl |
| author_facet | Guerrero López, Manuel Alejandro Gil Montoya, Consolación Gil Montoya, Francisco Alcayde García, Alfredo Baños Navarro, Raúl |
| author_sort | Guerrero López, Manuel Alejandro |
| collection | DSpace |
| description | 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 modularityas 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. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-8922 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-89222023-04-12T19:29:12Z Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks Guerrero López, Manuel Alejandro Gil Montoya, Consolación Gil Montoya, Francisco Alcayde García, Alfredo Baños Navarro, Raúl network optimization community detection modularity imbalance conductance multi-objective evolutionary algorithms 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 modularityas 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. 2020-11-23T12:08:08Z 2020-11-23T12:08:08Z 2020-11-17 info:eu-repo/semantics/article 2227-7390 http://hdl.handle.net/10835/8922 en https://www.mdpi.com/2227-7390/8/11/2048 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | network optimization community detection modularity imbalance conductance multi-objective evolutionary algorithms Guerrero López, Manuel Alejandro Gil Montoya, Consolación Gil Montoya, Francisco Alcayde García, Alfredo Baños Navarro, Raúl Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks |
| title | Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks |
| title_full | Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks |
| title_fullStr | Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks |
| title_full_unstemmed | Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks |
| title_short | Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks |
| title_sort | multi-objective evolutionary algorithms to find community structures in large networks |
| topic | network optimization community detection modularity imbalance conductance multi-objective evolutionary algorithms |
| url | http://hdl.handle.net/10835/8922 |
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