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Inferring structure in bipartite networks using the latent blockmodel and exact ICL

Published online by Cambridge University Press:  01 February 2017

JASON WYSE ,
NIAL FRIEL  and
PIERRE LATOUCHE
JASON WYSE
Affiliation:
Discipline of Statistics, School of Computer Science and Statistics, Trinity College Dublin, College Green, Dublin 2, Ireland (e-mail: [email protected])
NIAL FRIEL
Affiliation:
School of Mathematical Sciences and Insight: The National Centre for Big Data Analytics, University College Dublin, Belfield, Dublin 4, Ireland (e-mail: [email protected])
PIERRE LATOUCHE
Affiliation:
Laboratoire SAMM, Université Paris 1 Panthéon-Sorbonne, 90 rue de Tolbiac, F-75634 Paris Cedex 13, France (e-mail: [email protected])
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Abstract

We consider the task of simultaneous clustering of the two node sets involved in a bipartite network. The approach we adopt is based on use of the exact integrated complete likelihood for the latent blockmodel. Using this allows one to infer the number of clusters as well as cluster memberships using a greedy search. This gives a model-based clustering of the node sets. Experiments on simulated bipartite network data show that the greedy search approach is vastly more scalable than competing Markov chain Monte Carlo-based methods. Application to a number of real observed bipartite networks demonstrate the algorithms discussed.

Keywords

Bipartite networkbi-clusteringblockmodelmodel selectionintegrated complete likelihood
Type
Research Article
Information
Network Science , Volume 5 , Issue 1 , March 2017 , pp. 45 - 69
Copyright
Copyright © Cambridge University Press 2017 

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