A Bayesian multilevel model for populations of networks using exponential-family random graphs.

ABSTRACT: The collection of data on populations of networks is becoming increasingly common, where each data point can be seen as a realisation of a network-valued random variable. Moreover, each data point may be accompanied by some additional covariate information and one may be interested in assessing the effect of these covariates on network structure within the population. A canonical example is that of brain networks: a typical neuroimaging study collects one or more brain scans across multiple individuals, each of which can be modelled as a network with nodes corresponding to distinct brain regions and edges corresponding to structural or functional connections between these regions. Most statistical network models, however, were originally proposed to describe a single underlying relational

SUBMITTER: Lehmann B 

PROVIDER: S-EPMC11186958 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature