Inverse modelling-based multi-objective evolutionary algorithm with decomposition for community detection in complex networks

Feng Zou, Debao Chen, De-Shuang Huang, Renquan Lu and Xude Wang

Physica A: Statistical Mechanics and its Applications, 2019, vol. 513, issue C, 662-674

Abstract: Community structure is an important topological property of complex networks representing real-world systems, and it is believed to be a highly important tool for understanding how complex networks are organized and function. Generally, community detection can be considered to be a single-objective or multi-objective optimization problem, and a great number of population-based optimization algorithms have been explored to address this problem in the past several decades. In this study, we present a novel discrete inverse modelling-based multi-objective evolutionary algorithm with decomposition (DIM-MOEA/D) for community detection in complex networks. First, the population is initialized by a problem-specific method based on label propagation. Next, inverse models based on the network topology are constructed to generate offspring by sampling the objective space, and the problem-specific mutation is introduced to maintain the diversity of the population and avoid being trapped in the local optima. Next, the decomposition-based selection is introduced as the updating rule of individuals. Finally, several real-world networks are considered to evaluate the performance of the proposed algorithm. The experimental results demonstrate that compared with the state-of-the-art approaches, DIM-MOEA/D is an effective and promising method for solving community detection in complex networks.

Keywords: Community detection; Inverse modelling; Discrete; Multi-objective optimization; Decomposition (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:513:y:2019:i:c:p:662-674

DOI: 10.1016/j.physa.2018.08.077

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