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Title: A random effects stochastic block model for community detection in multiple networks with applications to neuroimaging Authors:  Subhadeep Paul - The Ohio State University (United States) [presenting]
Yuguo Chen - University of Illinois at Urbana-Champaign (United States)
Abstract: Motivated by multi-subject and multi-trial experiments in neuroimaging studies, we develop a modeling framework for joint community detection in a group of related networks, which can be considered as a sample from a population of networks. The proposed model, which we call the random effects stochastic block model, facilitates the study of group differences and subject specific variations in the community structure. The model proposes the existence of a putative mean community structure which is representative of the group or the population under consideration but is not the community structure of any individual component network. Instead, the community memberships of nodes vary in each component network with a transition matrix, thus modeling the variation in community structure across a group of subjects or trials. We propose two methods to estimate the quantities of interest, a variational EM algorithm and two model-free ``two-step" methods based on spectral and non-negative matrix factorization respectively. We also develop several resampling based hypothesis tests to test for differences in community structure in two populations of subjects both at the whole network level and node level. The methodology is applied to publicly available fMRI datasets from multi-subject experiments involving schizophrenia patients along with healthy controls.