Overlapping Stochastic Community Finding

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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
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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