Inferring structure in bipartite networks using the latent block model and exact ICL
|Title:||Inferring structure in bipartite networks using the latent block model and exact ICL||Authors:||Wyse, Jason
|Permanent link:||http://hdl.handle.net/10197/8414||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 learning;Statistics||DOI:||10.1017/nws.2016.25||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
Insight Research Collection
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.