One of the main goals in the statistical analysis of brain networks is clustering nodes, which represent brain regions, based on their connectivity patterns. This can be assisted by node covariates, such as lobe memberships. In the present paper, we cluster the nodes of a weighted brain network through a binomial extension of the extended stochastic block model by Legramanti et al. (2022b), which was originally designed for binary networks.
(2023). Binomial Extended Stochastic Block Model for Brain Networks . Retrieved from https://hdl.handle.net/10446/251929
Binomial Extended Stochastic Block Model for Brain Networks
Legramanti, Sirio;Argiento, Raffaele
2023-01-01
Abstract
One of the main goals in the statistical analysis of brain networks is clustering nodes, which represent brain regions, based on their connectivity patterns. This can be assisted by node covariates, such as lobe memberships. In the present paper, we cluster the nodes of a weighted brain network through a binomial extension of the extended stochastic block model by Legramanti et al. (2022b), which was originally designed for binary networks.File allegato/i alla scheda:
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