Using Triads to Identify Local Community Structure in Social Networks
- Justin Fagnan
- Osmar R. Zaiane, University of Alberta (Database)
- Denilson Barbosa
We present our novel community mining algorithm that uses only local information to accurately identify communities, outliers, and hubs in social networks. The main component of our algorithm is the T metric, which evaluates the relative quality of a community by considering the number of internal and external triads (3-node cliques) it contains. Furthermore we propose an intuitive statistical method based on our T metric, which correctly identifies outlier and hub nodes within each discovered community. Finally, we evaluate our approach on a series of groundtruth networks and show that our method outperforms the state-of-the-art in community mining algorithms.
Citation
J. Fagnan, O. Zaiane, D. Barbosa. "Using Triads to Identify Local Community Structure in 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{Fagnan+al:ASONAM14,
author = {Justin Fagnan and Osmar R. Zaiane and Denilson Barbosa},
title = {Using Triads to Identify Local Community Structure in Social
Networks},
booktitle = {IEEE/ACM International Conference on Social Networks Analysis
and Mining (ASONAM)},
year = 2014,
}Last Updated: November 14, 2019Submitted by Sabina P
