ANT COLONY OPTIMIZATION WITH A NEW RANDOM WALK MODEL FOR COMMUNITY DETECTION IN COMPLEX NETWORKS

Di Jin (), Dayou Liu (), Bo Yang (), Jie Liu () and Dongxiao He ()
Additional contact information
Di Jin: College of Computer Science and Technology, Jilin University, Changchun, 130012, China
Dayou Liu: College of Computer Science and Technology, Jilin University, Changchun, 130012, China
Bo Yang: College of Computer Science and Technology, Jilin University, Changchun, 130012, China
Jie Liu: College of Computer Science and Technology, Jilin University, Changchun, 130012, China
Dongxiao He: College of Computer Science and Technology, Jilin University, Changchun, 130012, China

Advances in Complex Systems (ACS), 2011, vol. 14, issue 05, 795-815

Abstract: Detecting communities from complex networks has recently triggered great interest. Aiming at this problem, a new ant colony optimization strategy building on the Markov random walks theory, which is named as MACO, is proposed in this paper. The framework of ant colony optimization is taken as the basic framework in this algorithm. In each iteration, a Markov random walk model is employed as heuristic rule; all of the ants' local solutions are aggregated to a global one through an idea of clustering ensemble, which then will be used to update a pheromone matrix. The strategy relies on the progressive strengthening of within-community links and the weakening of between-community links. Gradually this converges to a solution where the underlying community structure of the complex network will become clearly visible. The proposed MACO has been evaluated both on synthetic benchmarks and on some real-world networks, and compared with some present competing algorithms. Experimental result has shown that MACO is highly effective for discovering communities.

Keywords: Complex network; community detection; ant colony optimization; clustering ensemble; Markov random walk (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219525911003219
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:acsxxx:v:14:y:2011:i:05:n:s0219525911003219

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219525911003219

Access Statistics for this article

Advances in Complex Systems (ACS) is currently edited by Frank Schweitzer

More articles in Advances in Complex Systems (ACS) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().