Evolutionary link community structure discovery in dynamic weighted networks

Qiang Liu, Caihong Liu, Jiajia Wang, Xiang Wang, Bin Zhou and Peng Zou

Physica A: Statistical Mechanics and its Applications, 2017, vol. 466, issue C, 370-388

Abstract: Traditional community detection methods are often restricted in static network analysis. In fact, most of networks in real world obviously show dynamic characteristics with time passing. In this paper, we design a link community structure discovery algorithm in dynamic weighted networks, which can not only reveal the evolutionary link community structure, but also detect overlapping communities by mapping link communities to node communities. Meanwhile, our algorithm can also get the hierarchical structure of link communities by tuning a parameter. The proposed algorithm is based on weighted edge fitness and weighted partition density so as to determine whether to add a link to a community and whether to merge two communities to form a new link community. Experiments on both synthetic and real world networks demonstrate the proposed algorithm can detect evolutionary link community structure in dynamic weighted networks effectively.

Keywords: Link community structure; Dynamic networks; Weighted networks; Community evolution (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:466:y:2017:i:c:p:370-388

DOI: 10.1016/j.physa.2016.09.028

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