Title

Online Community Transition Detection

Publication Type

Conference Proceeding Article

Publication Date

6-2014

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. © 2014 Springer International Publishing Switzerland.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Web-Age Information Management: 15th International Conference, WAIM 2014, Macau, China, June 16-18, 2014: Proceedings

First Page

633

Last Page

644

ISBN

9783319080093

Identifier

10.1007/978-3-319-08010-9-68

Publisher

Springer Verlag

City or Country

Cham

Additional URL

http://dx.doi.org/10.1007/978-3-319-08010-9_68

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