In analyzing brain networks, it is of notable interest to cluster together nodes, representing brain regions, that share the same connectivity patterns, i.e., common parameters for the generative process of the edges, which in turn represent connections among brain regions. Based on the neuroscience theory that neighboring regions are more likely to connect, the anatomical coordinates of each region can be leveraged, together with edges, to guide the node partition, thus favoring clusters of neighboring regions with similar connectivity patterns. In light of this, to analyze the considered weighted brain network, we propose a two-fold generalization of the extended stochastic block model by [11]: (i) we adopt a Poisson likelihood for the edge weights, and (ii) we specify a spatial cohesion function that encourages neighboring regions to be clustered together. The performance of the proposed method on brain network data illustrates the potential gains of leveraging spatial node covariates in network clustering.

(2023). Extended Stochastic Block Model with Spatial Covariates for Weighted Brain Networks . Retrieved from https://hdl.handle.net/10446/260750

Extended Stochastic Block Model with Spatial Covariates for Weighted Brain Networks

Legramanti, Sirio;Argiento, Raffaele
2023-01-01

Abstract

In analyzing brain networks, it is of notable interest to cluster together nodes, representing brain regions, that share the same connectivity patterns, i.e., common parameters for the generative process of the edges, which in turn represent connections among brain regions. Based on the neuroscience theory that neighboring regions are more likely to connect, the anatomical coordinates of each region can be leveraged, together with edges, to guide the node partition, thus favoring clusters of neighboring regions with similar connectivity patterns. In light of this, to analyze the considered weighted brain network, we propose a two-fold generalization of the extended stochastic block model by [11]: (i) we adopt a Poisson likelihood for the edge weights, and (ii) we specify a spatial cohesion function that encourages neighboring regions to be clustered together. The performance of the proposed method on brain network data illustrates the potential gains of leveraging spatial node covariates in network clustering.
2023
Ghidini, Valentina; Legramanti, Sirio; Argiento, Raffaele
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/260750
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