doi: 10.4304/jsw.6.1.132-139
A Novel Approach for Finding Clusters from Complex Networks
- 1 Software College of Northeastern University, Shenyang, China
- 2 Department of Information Science in University of Arkansas at Little Rock, USA
Abstract
A feasible structural clustering method based on breadth-first-search is proposed for graphs. Clustering is very important and widely used in analyzing complex networks such as community identification. There are clusters with different shapes such as cliques and stars in practical application. Some existing algorithms can find clique-shaped clusters, but they are unable to identify starshaped clusters that are familiar in scale free networks. A feasible solution is provided to solve the problem. It is superior to other algorithms in one or several of the following aspects: An algorithm without any input parameters, Running time on a network with <em>n</em> nodes and <em>m</em> links is <em>O</em>(<em>n</em>), Extracting clusters of mixed shapes.
Keywords
- complex networks
- clustering
- modularity
How to Cite
Dongming Chen and Xiaowei Xu, "A Novel Approach for Finding Clusters from Complex Networks," Journal of Software, vol. 6, no. 1, pp. 132-139, 2011. https://doi.org/ 10.4304/jsw.6.1.132-139
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).