Inferring structure in bipartite networks using the latent block model and exact ICL

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Title: Inferring structure in bipartite networks using the latent block model and exact ICL
Authors: Wyse, Jason
Friel, Nial
Latouche, Pierre
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Date: 1-Feb-2017
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Cambridge University Press
Copyright (published version): 2017 Cambridge University Press
Keywords: Machine learningStatistics
DOI: 10.1017/nws.2016.25
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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