Testing for Association in Multi-View Network Data

• Activity Number: 168 - SLDS Student Paper Awards
• Type: Topic Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Lucy Gao* and Daniela Witten and Jacob Bien
• Companies: University of Washington and University of Washington and University of Southern California
• Keywords: Community detection; Data integration; Multi-view data; Node covariates; Stochastic block model

Abstract

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.