Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks

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 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.

Dynamic Community Detection Method of a Social Network Based on Node Embedding Representation

“…Li et al [19] proposed an improved label propagation algorithm named LPA-MNI in this study by combining the modularity function and node importance with the original LPA to solve the problem of community detection. Gerrero et al [20] proposed a new Pareto-based multi-objective evolutionary algorithm; they analyzed two multi-objective variants involving not only modularity but also the conductance metric and imbalance in the number of nodes of the communities. Lu et al [21] proposed a regression model for weighting the edges in the network, which maximizes the detection community based on modularity.…”

A Synergistic MOEA Algorithm with GANs for Complex Data Analysis

“…Traditional optimization methods, such as gradient-based methods, can only optimize one objective at a time, which may not be able to address MOPs effectively. In this context, multi-objective evolutionary algorithms (MOEAs) [5], which incorporate evolutionary algorithms (EAs) into MOPs, have emerged as a promising solution.…”

A multi-objective evolutionary algorithm based on mixed encoding for community detection

“…For example, community detection helps to find proteins with similar biological functions in protein-structure networks and helps to find people with the same hobbies in social networks. In the last decade, many researchers have been proposed a great number of community detection algorithm [8][9][10][11][12]. From the perspective of the number of optimization objective, the community detection optimization single objective and multi-objectives.…”