A Visual Data Mining Approach to Find Overlapping Communities in Networks
- Jiyang Chen
- Osmar R. Zaiane, University of Alberta (Database)
- Randy Goebel
Communities in social networks may overlap, with some hub nodes belonging to multiple communities. They may also have outliers, which are nodes that belong to no community. The criterion to locate hubs or outliers is network dependent. Previous methods usually require this information as input parameters, e.g., an expected number of communities, with no intuition or assistance. Here we present a visual data mining approach, which first helps the user to make appropriate parameter selections by observing initial data visualizations, and then finds and extracts overlapping community structures from the network. Experimental results verify the scalability and accuracy of our approach on real network data and show its advantages over previous methods.
Citation
J. Chen, O. Zaiane, R. Goebel. "A Visual Data Mining Approach to Find Overlapping Communities in Networks". IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM), Athens, Greece, (ed: Nasrullah Memon, Reda Alhajj), pp 338-343, 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 = {A Visual Data Mining Approach to Find Overlapping Communities in
Networks},
Editor = {Nasrullah Memon, Reda Alhajj},
Pages = {338-343},
booktitle = {IEEE/ACM International Conference on Social Networks Analysis
and Mining (ASONAM)},
year = 2009,
}Last Updated: January 15, 2020Submitted by Sabina P
