The detection of community structure in network via an improved spectral method

Fuding Xie, Min Ji, Yong Zhang and Dan Huang

Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, issue 15, 3268-3272

Abstract: Many networks of interest in the science, including social networks, computer networks and the World Wide Web, are found to be divided naturally into communities or groups. The problem of detecting communities is one of the outstanding issues in the study of network systems. Based on the improved shared nearest neighbor (SNN) similarity matrix, spectral method and fuzzy c-means (FCM) clustering algorithm, this paper proposes a new algorithm for detecting the communities in complex networks. The experiment reveals the validity of the presented method. The results are compared with other ones obtained by the different existing well methods and the conclusion is that the accuracy of the results calculated by this approach is much better than the known ones.

Keywords: Complex network; Community structure; Spectral method; SNN similarity matrix (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437109003409
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:388:y:2009:i:15:p:3268-3272

DOI: 10.1016/j.physa.2009.04.036

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 ().