Title
A divisive spectral method for network community detection
Abstract
Community detection is a fundamental problem in the domain of complex network analysis. It has received great attention, and many community detection methods have been proposed in the last decade. In this paper, we propose a divisive spectral method for identifying community structures from networks which utilizes a sparsification operation to pre-process the networks first, and then uses a repeated bisection spectral algorithm to partition the networks into communities. The sparsification operation makes the community boundaries clearer and sharper, so that the repeated spectral bisection algorithm extract high-quality community structures accurately from the sparsified networks. Experiments show that the combination of network sparsification and a spectral bisection algorithm is highly successful, the proposed method is more effective in detecting community structures from networks than the others.
Year
DOI
Venue
2015
10.1088/1742-5468/2016/03/033403
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
Keywords
Field
DocType
data mining (theory),analysis of algorithms,network dynamics,clustering techniques
Data mining,Bisection method,Network dynamics,Bisection,Quantum mechanics,Analysis of algorithms,Algorithm,Spectral method,Complex network analysis,Partition (number theory),Mathematics
Journal
Volume
Issue
ISSN
abs/1506.08354
3
1742-5468
Citations 
PageRank 
References 
4
0.43
16
Authors
6
Name
Order
Citations
PageRank
Jianjun Cheng1444.96
Longjie Li2213.16
mingwei leng350.79
weiguo lu440.43
Yukai Yao5202.07
Xiaoyun Chen66010.21