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Overlapping Stochastic Community Finding

Authors: 

Aaron McDaid, Neil Hurley, Brendan Murphy

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Community finding in social network analysis is the task of identifying groups of people within a larger population who are more likely to connect to each other than connect to others in the population. Much existing research has focussed on non-overlapping clustering. However, communities in realworld social networks do overlap. This paper introduces a new community finding method based on overlapping clustering. A Bayesian statistical model is presented, and a Markov Chain Monte Carlo (MCMC) algorithm is presented and evaluated in comparison with two existing overlapping community finding methods that are applicable to large networks. We evaluate our algorithm on networks with thousands of nodes and tens of thousands of edges.
Conference Name: 
The 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining
Proceedings: 
The 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining
Digital Object Identifer (DOI): 
10.na
Publication Date: 
17/08/2014
Conference Location: 
China
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No
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