A new genetic algorithm for community detection using matrix representation method

Kaiqi Chen and Weihong Bi

Physica A: Statistical Mechanics and its Applications, 2019, vol. 535, issue C

Abstract: Community structures contain important information of social networks. In most applications, mining community structures would be helpful for people to analyze networks. Typically, genetic algorithm is an effective approach to detect communities. At present, there are two kinds of genetic encoding methods: SGR (Tasgin et al., 2008) and LAR (Pizzuti, 2008) and both have some shortages, which always lead to premature convergence. Based on this, we proposed a new matrix encoding method for community detection, which contains total information of community clustering. We also designed crossover and mutation operator. According to the experiments, our method performed effectively.

Keywords: Complex network; Community detection; Genetic algorithm; Matrix encoding (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119313020
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:eee:phsmap:v:535:y:2019:i:c:s0378437119313020

DOI: 10.1016/j.physa.2019.122259

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().