Community detection in complex networks using multi-objective bat algorithm. (13th March 2020)

• Record Type: Journal Article
• Authors: Doush, Iyad Abu; Alrashdan, We'am Bilal; Al-Betar, Mohammed Azmi; Awadallah, Mohammed A.
• Is Part Of: International journal of mathematical modelling and numerical optimisation. Volume 10:Number 2(2019)
• Page Start: 123
• Page End: 140
• Publication Date: 2020-03-13
• Subjects: bat algorithm -- community detection -- multi-objective optimisation -- multi-objective bat algorithm -- MOBA; Mathematical models -- Periodicals; Mathematical optimization -- Periodicals; 519.605
• Languages: English
• ISSNs: 2040-3607

Abstract

Community detection is the problem of identifying communities in which we aim to discover groups of nodes with high connectivity within the same group and with low connectivity outside the group. Community detection is considered to be a non-deterministic polynomial-time hard problem. Heuristic algorithms can be used to solve such a complex optimisation problem. Bat algorithm (BA) is a meta-heuristic optimisation algorithm. The BA can be used to model a multi-objective optimisation problem. In this paper, the multi-objective bat algorithm (MOBA) is adapted to model and solve the community detection problem. In order to evaluate the algorithm, four real-world datasets are used. The performance of the algorithm is compared with seven other methods from the literature. The comparison was in terms of two metrics to check the quality of the obtained community namely modularity (Q) and normalised mutual information (NMI). The results show that the proposed algorithm outperforms all algorithms in one dataset and that it is competitive in other cases.