By Topic

Analyzing group dynamics for incidental topics in online social networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Yadong Zhou ; MOE KLINNS Lab and SKLMS Lab, Xi'an Jiaotong University, China 710049 ; Xiaohong Guan ; Qinghua Zheng ; Qindong Sun
more authors

Groups discussing popular topics in online social networks are of great interests recently. In this paper, we measure the dynamics of the online groups discussing incidental popular topics and present method for predicting the dynamic sizes of incidental topic groups. It is found that the dynamic sizes of incidental topic groups follow the law of heavy-tail. Based on the heavy-tailed theory a prediction method is developed for analyzing the dynamics of this type of groups. The models and methods developed in the paper are validated using the actual data from SOHU blog sites, one of the most influential blog sites in China. The experiment results show that the method can predict the dynamic size of incidental topic groups with both short and long time scales.

Published in:

Intelligent Control and Automation (WCICA), 2010 8th World Congress on

Date of Conference:

7-9 July 2010