Persistent community detection in dynamic social networks

Siyuan Liu; Shuhui Wang; Ramayya Krishnan

doi:10.1007/978-3-319-06608-0_7

Persistent community detection in dynamic social networks

Siyuan Liu, Shuhui Wang, Ramayya Krishnan

Supply Chain and Information Systems

Research output: Contribution to journal › Conference article › peer-review

9 Scopus citations

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 language	English (US)
Pages (from-to)	78-89
Number of pages	12
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8443 LNAI
Issue number	PART 1
DOIs	https://doi.org/10.1007/978-3-319-06608-0_7
State	Published - 2014
Event	18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China Duration: May 13 2014 → May 16 2014

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

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10.1007/978-3-319-06608-0_7

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title = "Persistent community detection in dynamic social networks",

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.",

author = "Siyuan Liu and Shuhui Wang and Ramayya Krishnan",

year = "2014",

doi = "10.1007/978-3-319-06608-0_7",

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volume = "8443 LNAI",

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publisher = "Springer Science and Business Media Deutschland GmbH",

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T1 - Persistent community detection in dynamic social networks

AU - Liu, Siyuan

AU - Wang, Shuhui

AU - Krishnan, Ramayya

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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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DO - 10.1007/978-3-319-06608-0_7

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SN - 0302-9743

VL - 8443 LNAI

SP - 78

EP - 89

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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T2 - 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014

Y2 - 13 May 2014 through 16 May 2014

ER -

Persistent community detection in dynamic social networks

Abstract

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