Overlapping Stochastic Community Finding
|Title:||Overlapping Stochastic Community Finding||Authors:||McDaid, Aaron
Hurley, Neil J.
Murphy, Thomas Brendan
|Permanent link:||http://hdl.handle.net/10197/8215||Date:||20-Aug-2014||Online since:||2016-12-14T11:06:05Z||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.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2014 IEEE||Keywords:||Machine learning; Statistics||DOI:||10.1109/ASONAM.2014.6921554||Language:||en||Status of Item:||Peer reviewed||Conference Details:||The 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), Beijing, China, 17-20 August 2014|
|Appears in Collections:||Computer Science Research Collection|
Mathematics and Statistics Research Collection
Insight Research Collection
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