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).