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2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
ISBN: 978-1-5090-2847-4
pp: 319-322
Neil Hurley , Insight Centre for Data Analytics, University College Dublin
Erika Duriakova , Insight Centre for Data Analytics, University College Dublin
ABSTRACT
Blockmodelling is a technique whose aim is to identify meaningful structure in networks. Community finding is a type of blockmodelling in so far as it focuses on identifying dense subgraph structure. Generalised blockmodelling allows an analyst to explicitly control the type of extracted structure. When compared to the well studied community-finding problem, generalised blockmodelling algorithms lag well behind in terms of their scalability. In this paper we formulate and evaluate a generalised blockmodelling algorithm, based on the Infomap information-theoretic community-finding algorithm. We reformulate the optimisation objective of the Infomap algorithm, so that it is extended to identify specific types of meso-scale structure that are given as input by the analyst. We evaluate our method against other generalised blockmodelling algorithms.
INDEX TERMS
Mathematical model, Algorithm design and analysis, Partitioning algorithms, Social network services, Optimization, Image edge detection, Pattern matching
CITATION

N. Hurley and E. Duriakova, "An information theoretic approach to generalised blockmodelling for the identification of meso-scale structure in networks," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016, pp. 319-322.
doi:10.1109/ASONAM.2016.7752252
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