| Full text | |
| Author(s): |
Cerqueira, Andressa
;
Gallo, Sandro
;
Leonardi, Florencia
;
Vera, Cristel
Total Authors: 4
|
| Document type: | Journal article |
| Source: | ALEA-LATIN AMERICAN JOURNAL OF PROBABILITY AND MATHEMATICAL STATISTICS; v. 21, p. 26-pg., 2024-01-01. |
| Abstract | |
The Degree Corrected Stochastic Block Model (DCSBM) is a probabilistic model for random networks with community structure in which vertices of the same community are allowed to have distinct degree distributions. On the modeling side, this property makes the DCSBM more suitable for real-life complex networks. On the statistical side, it is more challenging due to a large number of parameters. In this paper, we prove that the penalized marginal likelihood estimator, when assuming prior distributions for the parameters, is strongly consistent for estimating the number of communities. We consider dense or semi-sparse random networks, and our estimator is unbounded, in the sense that the number of communities k considered can be as big as n, the number of nodes in the network. (AU) | |
| 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: | 17/10555-0 - Stochastic modeling of interacting systems |
| Grantee: | Fabio Prates Machado |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 19/23439-4 - Stochastic chains with unbounded memory and random walks on graphs |
| Grantee: | Alexsandro Giacomo Grimbert Gallo |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 23/05857-9 - Statistical Analysis of Complex Networks |
| Grantee: | Andressa Cerqueira |
| Support Opportunities: | Regular Research Grants |