Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks
MetadataShow full item record
Author/sGuerrero López, Manuel Alejandro; Gil Montoya, Consolación; Gil Montoya, Francisco; Alcayde García, Alfredo; Baños Navarro, Raúl
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-obje...
multi-objective evolutionary algorithms