Inferring overlapping community structure with degree-corrected block model

Yingfei Qu, Weiren Shi and Xin Shi

Physica A: Statistical Mechanics and its Applications, 2015, vol. 419, issue C, 48-54

Abstract: Recent research has shown great interest in statistical inference methods for community detection, not only in models and algorithms but also in the detectability. In this paper we propose a fast community detection algorithm based on the degree-corrected block model. By introducing a parameter to select the candidate solutions, our algorithm is able to detect overlapping communities. Experiments on a range of networks have achieved state-of-the-art results. Moreover, we show that the algorithm based on the degree-corrected block model also suffers the detectability limitation, which is in accord with the most recent research on the detectability threshold.

Keywords: Community detection; Stochastic block model; Spectral partitioning; Maximum likelihood method (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437114008632
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:419:y:2015:i:c:p:48-54

DOI: 10.1016/j.physa.2014.10.025

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().