Abstract:
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We consider data consisting of multiple networks, each comprised of a different edge set on a common set of nodes. Many models have been proposed for the analysis of such multi-view network data, under the assumption that the data views are closely related. We provide tools for evaluating this assumption. In particular, we ask: given that each network follows a stochastic block model, is there an association between the latent community memberships of the nodes in the two networks? To answer this question, we extend the stochastic block model for a single network view to the two-view setting, and develop a new hypothesis test for the null hypothesis that the latent community memberships in the two data views are independent. We apply our test to protein-protein interaction data from the HINT database (Das and Yu, 2012b). We find evidence of a weak association between the latent community memberships of proteins defined with respect to binary interaction data and the latent community memberships of proteins defined with respect to co-complex association data. We also extend this proposal to the setting of a network with node covariates.
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