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

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  • WYSE, JASON
  • FRIEL, NIAL
  • LATOUCHE, PIERRE

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.

Suggested Citation

  • Wyse, Jason & Friel, Nial & Latouche, Pierre, 2017. "Inferring structure in bipartite networks using the latent blockmodel and exact ICL," Network Science, Cambridge University Press, vol. 5(1), pages 45-69, March.
    • Handle: RePEc:cup:netsci:v:5:y:2017:i:01:p:45-69_00
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    Cited by:

    1. C. Biernacki & J. Jacques & C. Keribin, 2023. "A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 332-381, July.
    2. Etienne Côme & Nicolas Jouvin & Pierre Latouche & Charles Bouveyron, 2021. "Hierarchical clustering with discrete latent variable models and the integrated classification likelihood," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 957-986, December.
    3. Bergé, Laurent R. & Bouveyron, Charles & Corneli, Marco & Latouche, Pierre, 2019. "The latent topic block model for the co-clustering of textual interaction data," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 247-270.
    4. Valerie Robert & Yann Vasseur & Vincent Brault, 2021. "Comparing High-Dimensional Partitions with the Co-clustering Adjusted Rand Index," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 158-186, April.
    5. Watanabe, Chihiro & Suzuki, Taiji, 2021. "Goodness-of-fit test for latent block models," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    6. Alessandro Casa & Charles Bouveyron & Elena Erosheva & Giovanna Menardi, 2021. "Co-clustering of Time-Dependent Data via the Shape Invariant Model," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 626-649, October.

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