doi: 10.4304/jsw.6.12.2441-2448
Role Assorted Community Discovery for Weighted Networks
- 1 School of software, Dalian University of Technology, Dalian, China
- 2 School 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.
Keywords
- community discovery
- role assorted thoug
How to Cite
Ruixin Ma, Guishi Deng, and Xiao Wang, "Role Assorted Community Discovery for Weighted Networks," Journal of Software, vol. 6, no. 12, pp. 2441-2448, 2011. https://doi.org/ 10.4304/jsw.6.12.2441-2448
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).