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Weighted stochastic block model

Author

Listed:
  • Tin Lok James Ng

    (Trinity College Dublin)

  • Thomas Brendan Murphy

    (University College Dublin)

Abstract

We propose a weighted stochastic block model (WSBM) which extends the stochastic block model to the important case in which edges are weighted. We address the parameter estimation of the WSBM by use of maximum likelihood and variational approaches, and establish the consistency of these estimators. The problem of choosing the number of classes in a WSBM is addressed. The proposed model is applied to simulated data and an illustrative data set.

Suggested Citation

  • Tin Lok James Ng & Thomas Brendan Murphy, 2021. "Weighted stochastic block model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1365-1398, December.
    • Handle: RePEc:spr:stmapp:v:30:y:2021:i:5:d:10.1007_s10260-021-00590-6
    DOI: 10.1007/s10260-021-00590-6
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    References listed on IDEAS

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    1. D. S. Choi & P. J. Wolfe & E. M. Airoldi, 2012. "Stochastic blockmodels with a growing number of classes," Biometrika, Biometrika Trust, vol. 99(2), pages 273-284.
    2. Ludkin, Matthew, 2020. "Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    3. Christophe Ambroise & Catherine Matias, 2012. "New consistent and asymptotically normal parameter estimates for random‐graph mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 3-35, January.
    4. Zhi-Sheng Ye & Nan Chen, 2017. "Closed-Form Estimators for the Gamma Distribution Derived From Likelihood Equations," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 177-181, April.
    Full references (including those not matched with items on IDEAS)

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