| Full text | |
| Author(s): |
Cerqueira, Andressa
;
Leonardi, Florencia
Total Authors: 2
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON INFORMATION THEORY; v. 66, n. 10, p. 10-pg., 2020-10-01. |
| Abstract | |
In this article we introduce an estimator for the number of communities in the Stochastic Block Model (SBM), based on the maximization of a penalized version of the so-called Krichevsky-Trofimov mixture distribution. We prove its eventual almost sure convergence to the underlying number of communities, without assuming a known upper bound on that quantity. Our results apply to both the dense and the sparse regimes. To our knowledge this is the first consistency result for the estimation of the number of communities in the SBM in the unbounded case, that is when the number of communities is allowed to grow with the same size. (AU) | |
| FAPESP's process: | 19/17734-3 - Model selection in high dimensions: theoretical properties and applications |
| Grantee: | Florencia Graciela Leonardi |
| Support Opportunities: | Research Grants - eScience and Data Science Program - Regular Program Grants |
| FAPESP's process: | 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat |
| Grantee: | Oswaldo Baffa Filho |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 15/12595-4 - Perfect simulation of Markov random fields on graphs |
| Grantee: | Andressa Cerqueira |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |