Persistent Community Detection in Dynamic Social Networks

Citation data:

Advances in Knowledge Discovery and Data Mining, ISSN: 0302-9743, Vol: 8443 LNAI, Issue: PART 1, Page: 78-89

Publication Year:
2014
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Repository URL:
http://ink.library.smu.edu.sg/sis_research/3479; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4480&context=sis_research
DOI:
10.1007/978-3-319-06608-0_7
Author(s):
LIU, Siyuan; WANG, Shuhui; KRISHNAN, Ramayya
Publisher(s):
Springer Nature; Springer
Tags:
Mathematics; Computer Science; Community detection; persistent behavior; social networks; Computer Sciences; Theory and Algorithms
book chapter description
While community detection is an active area of research in social network analysis, little effort has been devoted to community detection using time-evolving social network data. We propose an algorithm, Persistent Community Detection (PCD), to identify those communities that exhibit persistent behavior over time, for usage in such settings. Our motivation is to distinguish between steady-state network activity, and impermanent behavior such as cascades caused by a noteworthy event. The results of extensive empirical experiments on real-life big social networks data show that our algorithm performs much better than a set of baseline methods, including two alternative models and the state-of-the-art. © 2014 Springer International Publishing.