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/).
Cite this article
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 -