Community Detection in Complex Networks Using Improved Artificial Bee Colony Algorithm

DOI

10.2991/cimns-16.2016.71How to use a DOI?

Keywords

component; complex network; artificial bee colony algorithm; community detection

Abstract

With more and more various systems in nature and society are proved to be modeled as complex networks, community detection in complex networks as a fundamental problem becomes a hot research topic in a large scale of subjects. Artificial Bee Colony Algorithm (ABC) has high efficiency and does not require any prior knowledge about the number or the original division of the communities. So it is suitable to solve complex clustering problems. We propose an improved ABC algorithm which modifies the number of initial food sources and dynamically adjusts search scope. Experimental results show that our algorithm can discover communities effectively by the classic Zachary Karate Club network. By comparative experiments, the improved artificial bee colony algorithm outperforms the traditional ABC algorithm in complex network.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ziyang Wang
AU  - Xiaolin Zhao
AU  - Peiyuan Wen
AU  - Jingfeng Xue
AU  - Changzhen Hu
PY  - 2016/09
DA  - 2016/09
TI  - Community Detection in Complex Networks Using Improved Artificial Bee Colony Algorithm
BT  - Proceedings of the 2016 International Conference on Communications, Information Management and Network Security
PB  - Atlantis Press
SP  - 283
EP  - 288
SN  - 2352-538X
UR  - https://doi.org/10.2991/cimns-16.2016.71
DO  - 10.2991/cimns-16.2016.71
ID  - Wang2016/09
ER  -