Translator Disclaimer
2019 Consistency of the maximum likelihood and variational estimators in a dynamic stochastic block model
Léa Longepierre, Catherine Matias
Electron. J. Statist. 13(2): 4157-4223 (2019). DOI: 10.1214/19-EJS1624

Abstract

We consider a dynamic version of the stochastic block model, in which the nodes are partitioned into latent classes and the connection between two nodes is drawn from a Bernoulli distribution depending on the classes of these two nodes. The temporal evolution is modeled through a hidden Markov chain on the nodes memberships. We prove the consistency (as the number of nodes and time steps increase) of the maximum likelihood and variational estimators of the model parameters, and obtain upper bounds on the rates of convergence of these estimators. We also explore the particular case where the number of time steps is fixed and connectivity parameters are allowed to vary.

Citation

Download Citation

Léa Longepierre. Catherine Matias. "Consistency of the maximum likelihood and variational estimators in a dynamic stochastic block model." Electron. J. Statist. 13 (2) 4157 - 4223, 2019. https://doi.org/10.1214/19-EJS1624

Information

Received: 1 April 2019; Published: 2019
First available in Project Euclid: 22 October 2019

zbMATH: 07136615
MathSciNet: MR4021264
Digital Object Identifier: 10.1214/19-EJS1624

Subjects:
Primary: 62F12

JOURNAL ARTICLE
67 PAGES


SHARE
Vol.13 • No. 2 • 2019
Back to Top