Local Community Identification in Social Networks
- Jiyang Chen
- Osmar R. Zaiane, University of Alberta (Database)
- Randy Goebel
There has been much recent research on identifying global community structure in networks. However, most existing approaches require complete information of the graph in question, which is impractical for some networks, e.g. the World Wide Web (WWW). Algorithms for local community detection have been proposed but their results usually contain many outliers. In this paper, we propose a new measure of local community structure, coupled with a two-phase algorithm that extracts all possible candidates first, and then optimizes the community hierarchy. We compare our results with previous methods on real world networks such as the co-purchase network from Amazon. Experimental results verify the feasibility and effectiveness of our approach.
Citation
J. Chen, O. Zaiane, R. Goebel. "Local Community Identification in Social Networks". IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM), Athens, Greece, (ed: Nasrullah Memon, Reda Alhajj), pp 237-242, July 2009.| Keywords: | |
| Category: | In Conference |
| Web Links: | IEEE |
BibTeX
@incollection{Chen+al:ASONAM09,
author = {Jiyang Chen and Osmar R. Zaiane and Randy Goebel},
title = {Local Community Identification in Social Networks},
Editor = {Nasrullah Memon, Reda Alhajj},
Pages = {237-242},
booktitle = {IEEE/ACM International Conference on Social Networks Analysis
and Mining (ASONAM)},
year = 2009,
}Last Updated: January 15, 2020Submitted by Sabina P
