Publication Type
Conference Proceeding Article
Version
publishedVersion
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 | Social Media
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
Citation
TAN, Biying; ZHU, Feida; QU, Qiang; and LIU, Siyuan.
Online Community Transition Detection. (2014). Web-Age Information Management: 15th International Conference, WAIM 2014, Macau, China, June 16-18, 2014: Proceedings. 633-644.
Available at: https://ink.library.smu.edu.sg/sis_research/3145
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-319-08010-9_68