Volume 6 Number 12 (Dec. 2011)
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JSW 2011 Vol.6(12): 2441-2448 ISSN: 1796-217X
doi: 10.4304/jsw.6.12.2441-2448

Role Assorted Community Discovery for Weighted Networks

Ruixin Ma1, Guishi Deng2, Xiao Wang2
1School of software, Dalian University of Technology, Dalian, China
2School of Management Science and Engineering, Dalian University of Technology, Dalian, China

Abstract—This paper considers the difficulties in community discovery, and comes up with a community discovery algorithm on the basis of role assorted thoughts. Previous work indicates that a robust approach to community detection is the maximization of inner communication and the minimization of the inout interaction. Here we show that this problem can be solved accords to the role assorted method which give distinguish labels to vertices in the same community. This method leads us to a number of possible algorithms for detecting community structures in both unweighted and weighted networks. The applicability and expandability of algorithms proposed are illustrated with application to a variety of computergenerated networks and realworld complex networks.

Index Terms—community discovery, role assorted thoughts, robust approach, distinguish label


Cite: Ruixin Ma, Guishi Deng, Xiao Wang, "Role Assorted Community Discovery for Weighted Networks," Journal of Software vol. 6, no. 12, pp. 2441-2448, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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