Home > Archive > 2020 > Volume 10 Number 2 (Feb. 2020) >
IJMLC 2020 Vol.10(2): 323-329 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.938

Modularity Based ABC Algorithm for Detecting Communities in Complex Networks

Thet Thet Aung and Thi Thi Soe Nyunt

Abstract—Network structure is a representation of interactions among actors. Transportation network, biological network and social networks have complex networks structure. Detecting community in complex networks is an important research to gain insight information. Effective optimization algorithms are needed in network community detection. Modularity based artificial bee colony (MABC) algorithm is proposed to uncover community in complex network. The proposed MABC algorithm is evaluated by Modularity and Normalize Mutual Information (NMI) metrics. Three real world datasets are used in the experiment. The proposed approach effectively detects community structure and produces noticeable good result than other previous algorithms in sample complex networks.

Index Terms—Artificial bee colony, community detection, modularity and normalize mutual information.

The authors are Cloud Lab Research Lab of University of Computer Studies, Yangon, Myanmar. (e-mail: thetthetaung@ucsy.edu.mm, thithi@ucsy.edu.mm).


Cite: Thet Thet Aung and Thi Thi Soe Nyunt, "Modularity Based ABC Algorithm for Detecting Communities in Complex Networks," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 323-329, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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