Community Evolution Prediction in Dynamic Social Networks
- Mansoureh Takaffoli
- Reihaneh Rabbany
- Osmar R. Zaiane, University of Alberta (Database)
Finding patterns of interaction and predicting the future structure of networks has many important applications, such as recommendation systems and customer targeting. Community structure of social networks may undergo different temporal events and transitions. In this paper, we propose a framework to predict the occurrence of different events and transition for communities in dynamic social networks. Our framework incorporates key features related to a community – its structure, history, and influential members, and automatically detects the most predictive features for each event and transition. Our experiments on real world datasets confirms that the evolution of communities can be predicted with a very high accuracy, while we further observe that the most significant features vary for the predictability of each event and transition.
Citation
M. Takaffoli, R. Rabbany, O. Zaiane. "Community Evolution Prediction in Dynamic Social Networks". IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM), Beijing, China, August 2014.| Keywords: | |
| Category: | In Conference |
| Web Links: | Webdocs |
BibTeX
@incollection{Takaffoli+al:ASONAM14,
author = {Mansoureh Takaffoli and Reihaneh Rabbany and Osmar R. Zaiane},
title = {Community Evolution Prediction in Dynamic Social Networks},
booktitle = {IEEE/ACM International Conference on Social Networks Analysis
and Mining (ASONAM)},
year = 2014,
}Last Updated: November 13, 2019Submitted by Sabina P
