Persistent Community Detection in Dynamic Social Networks

Persistent Community Detection in Dynamic Social Networks

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 beha...

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Bibliographic Details
Main Authors: LIU, Siyuan, WANG, Shuhui, KRISHNAN, Ramayya
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
Subjects:
Community detection
persistent behavior
social networks
Computer Sciences
Theory and Algorithms
Online Access:https://ink.library.smu.edu.sg/sis_research/3479
https://ink.library.smu.edu.sg/context/sis_research/article/4480/viewcontent/C101___Persistent_Community_Detection_in_Dynamic_Social_Networks__PAKDD2014_.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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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