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Activity Number: 48 - New Frontiers in High-Dimensional and Complex Data analyses
Type: Invited
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #300457
Title: Network Response Regression for Modeling Population of Networks with Covariates
Author(s): Emma Jingfei Zhang* and Will Wei Sun and Lexin Li
Companies: University of Miami and Purdue University and University of California at Berkeley
Keywords: multiple networks; non-convex optimization; brain connectivity analysis; community detection; tensor; network response regression

Multiple-network data are fast emerging in recent years, where a separate network over a common set of nodes is measured for each individual subject, along with rich subject covariates information. Existing network analysis methods have primarily focused on modeling a single network, and are not directly applicable to multiple networks with subject covariates. In this work, we propose a new network response regression model, where the observed networks are treated as matrix-valued responses, and the individual covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the parsimonious effects of subject covariates through a sparse slope tensor. We formulate the parameter estimation as a non-convex optimization problem, and develop an efficient alternating gradient descent algorithm. We establish the non-asymptotic error bound for the actual estimator from our optimization algorithm. Built upon this error bound, we derive the strong consistency for network community recovery, as well as the edge selection consistency. We demonstrate the efficacy of our method through two brain connectivity studies.

Authors who are presenting talks have a * after their name.

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