Multi-objective optimization for community detection in multilayer networks

¹ College of Computer and Information Science, Southwest University - Chongqing 400715, China
² School of Mechanical Engineering and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University - Xi'an 710072, Shaanxi, China
³ Faculty of Natural Sciences and Mathematics, University of Maribor - Koroška cesta 160, 2000 Maribor, Slovenia
⁴ Department of Medical Research, China Medical University Hospital, China Medical University Taichung 404332, Taiwan
⁵ Alma Mater Europaea ECM - Slovenska ulica 17, 2000 Maribor, Slovenia

^(a) zhenwang0@gmail.com
^(b) cgao@swu.edu.cn (corresponding author)

Received: 10 April 2021
Accepted: 10 June 2021

Abstract

Community detection in multilayer networks plays a key role in revealing the multiple aspects of information spreading and in comprehending the relationships and interactions within and between each layer. However, most existing algorithms are prone to local optimality, and they are also difficult to extend to high-dimensional networks. To address these challenges, we propose here a multi-objective algorithm for community detection that is based on the genetic algorithm. In particular, the modularity is introduced to optimize each network layer iteratively, and the local search is combined with genetic operations to overcome local optimality. Comparative benchmarks with other algorithms on artificial and real-world networks show that the proposed algorithm performs better, especially on high-dimensional networks.