A fast clustering algorithm for mining social network data
Social and Information Networks
2014-09-01 v2 Physics and Society
Abstract
Many groups with diverse convictions are interacting online. Interactions in online communities help people to engage each other and enhance understanding across groups. Online communities include multiple sub-communities whose members are similar due to social ties, characteristics, or ideas on a topic. In this research, we are interested in understanding the changes in the relative size and activity of these sub-communities, their merging or splitting patterns, and the changes in the perspectives of the members of these sub-communities due to endogenous dynamics inside the community.
Cite
@article{arxiv.1403.1214,
title = {A fast clustering algorithm for mining social network data},
author = {Saeede Ajorlou and Issac Shams and Kai Yang},
journal= {arXiv preprint arXiv:1403.1214},
year = {2014}
}
Comments
This paper has been withdrawn by the author due to a crucial sign error in figures