By Topic

Toward Predicting Collective Behavior via Social Dimension Extraction

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
$31 $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

2 Author(s)
Lei Tang ; Arizona State Univ., Tempe, AZ, USA ; Huan Liu

The SocioDim framework demonstrates promising results toward predicting collective behavior. However, many challenges require further research. For example, networks in social media are continually evolving, with new members joining a network and new connections established between existing members each day. This dynamic nature of networks entails efficient update of the model for collective behavior prediction. It is also intriguing to consider temporal fluctuation into the problem of collective behavior prediction.

Published in:

Intelligent Systems, IEEE  (Volume:25 ,  Issue: 4 )