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2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2014)
China
Aug. 17, 2014 to Aug. 20, 2014
ISBN: 978-1-4799-5877-1
pp: 17-20
Aaron McDaid , Insight Centre for Data Analytics, School of Computer Science and Informatics, University College Dublin, Ireland
Neil Hurley , Insight Centre for Data Analytics, School of Computer Science and Informatics, University College Dublin, Ireland
Brendan Murphy , Insight Centre for Data Analytics, School of Mathematical Sciences, University College Dublin, Ireland
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 real-world 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.
INDEX TERMS
Communities, Clustering algorithms, Stochastic processes, Educational institutions, Computational modeling, Proposals, Social network services
CITATION

A. McDaid, N. Hurley and B. Murphy, "Overlapping Stochastic Community Finding," 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), China, 2014, pp. 17-20.
doi:10.1109/ASONAM.2014.6921554
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