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An algorithm J-SC of detecting communities in complex networks.
- Source :
-
Physics Letters A . 11/13/2017, Vol. 381 Issue 42, p3604-3612. 9p. - Publication Year :
- 2017
-
Abstract
- Currently, community detection in complex networks has become a hot-button topic. In this paper, based on the Spectral Clustering (SC) algorithm, we introduce the idea of Jacobi iteration, and then propose a novel algorithm J-SC for community detection in complex networks. Furthermore, the accuracy and efficiency of this algorithm are tested by some representative real-world networks and several computer-generated networks. The experimental results indicate that the J-SC algorithm can accurately and effectively detect the community structure in these networks. Meanwhile, compared with the state-of-the-art community detecting algorithms SC, SOM, K-means, Walktrap and Fastgreedy, the J-SC algorithm has better performance, reflecting that this new algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SOM and Walktrap algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03759601
- Volume :
- 381
- Issue :
- 42
- Database :
- Academic Search Index
- Journal :
- Physics Letters A
- Publication Type :
- Academic Journal
- Accession number :
- 125546673
- Full Text :
- https://doi.org/10.1016/j.physleta.2017.09.013