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

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.

• Publication year

  2014

• Venue

  Network Science

• Publication date

  2014-04-10

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1017/nws.2016.25arXiv 1404.2911
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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