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GWBM: an algorithm based on grey wolf optimization and balanced modularity for community discovery in social networks

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Abstract

One of the crucial research areas in the analysis of complex social networks is the identification of communities. Since community detection is an NP-complete problem, numerous meta-heuristic approaches have been used for this problem, mostly taking “modularity” as the objective function. However, modularity-based optimization methods suffer from resolution limit. In this paper, a novel community detection algorithm is proposed that aims to optimize a newly introduced fitness function “balanced modularity.” The proposed method, called grey wolf balanced modularity (GWBM), uses fast label propagation algorithm to form the initial population, relatively novel grey wolf algorithm as the main optimizer, and a problem-specific variant of simulated annealing algorithm for local search. The experiments on synthetic and known real-world networks, including Karate, American Football, and Facebook, have shown that GWBM is accurate and comparable with the state-of-the-art community detection methods.

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Authors and Affiliations

  1. Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

    Ehsan Jokar, Mohammad Mosleh & Mohammad Kheyrandish

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  1. Ehsan Jokar
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  2. Mohammad Mosleh
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  3. Mohammad Kheyrandish
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Correspondence to Mohammad Mosleh.

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Jokar, E., Mosleh, M. & Kheyrandish, M. GWBM: an algorithm based on grey wolf optimization and balanced modularity for community discovery in social networks. J Supercomput 78, 7354–7377 (2022). https://doi.org/10.1007/s11227-021-04174-9

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