Persistent community detection in dynamic social networks

Siyuan Liu, Shuhui Wang, Ramayya Krishnan

Research output: Contribution to journalConference article

4 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)78-89
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8443 LNAI
Issue numberPART 1
DOIs
StatePublished - Jan 1 2014
Event18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China
Duration: May 13 2014May 16 2014

Fingerprint

Community Detection
Dynamic Networks
Social Networks
Electric network analysis
Social Network Analysis
Cascade
Baseline
Experiments
Alternatives
Experiment
Model

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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abstract = "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.",
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Persistent community detection in dynamic social networks. / Liu, Siyuan; Wang, Shuhui; Krishnan, Ramayya.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8443 LNAI, No. PART 1, 01.01.2014, p. 78-89.

Research output: Contribution to journalConference article

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