Online community transition detection

Biying Tan, Feida Zhu, Qiang Qu, Siyuan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Mining user behavior patterns in social networks is of great importance in user behavior analysis, targeted marketing, churn prediction and other applications. However, less effort has been made to study the evolution of user behavior in social communities. In particular, users join and leave communities over time. How to automatically detect the online community transitions of individual users is a research problem of immense practical value yet with great technical challenges. In this paper, we propose an algorithm based on the Minimum Description Length (MDL) principle to trace the evolution of community transition of individual users, adaptive to the noisy behavior. Experiments on real data sets demonstrate the efficiency and effectiveness of our proposed method.

Original languageEnglish (US)
Title of host publicationWeb-Age Information Management - 15th International Conference, WAIM 2014, Proceedings
PublisherSpringer Verlag
Pages633-644
Number of pages12
ISBN (Print)9783319080093
DOIs
StatePublished - 2014
Event15th International Conference on Web-Age Information Management, WAIM 2014 - Macau, China
Duration: Jun 16 2014Jun 18 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8485 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Conference on Web-Age Information Management, WAIM 2014
CountryChina
CityMacau
Period6/16/146/18/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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