Community detection in random network models for modeling brain data

• Grant number: 25/09861-6
• Support Opportunities: Scholarships in Brazil - Master
• Start date: July 01, 2025
• End date: July 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Probability
• Principal Investigator: Florencia Graciela Leonardi
• Grantee: Daniela Sano Adathi
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat, AP.CEPID

Abstract

The problem of community detection in random networks involves grouping the nodes of a network into communities with similar statistical properties, given only the connection patterns (edges) of the random graph. When the edges represent distances between points or connection intensities, the problem is also known as clustering, a central problem in statistical learning, data science, and artificial intelligence. Among the probabilistic models with community structure, the Stochastic Block Model is the most studied in recent literature. This model has also been applied to modeling brain data. This proposal aims to study the Stochastic Block Model and the recently developed algorithms, and to understand their probabilistic and statistical properties, as well as their potential applications in modeling brain data. The research that will be developed consists mainly of a review of the recent literature on this model and its extensions, the implementation of community detection and model selection methods, and their application to the data generated within the CEPID NeuroMat project. The computational tools developed in the context of this project will be made available as open-source software in public repositories. (AU)