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Activity Number: 471 - Advances in High-Dimensional Inference and Multiple Testing
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #304809
Title: Two-Sample Tests for Graphs with Applications in Neuroscience
Author(s): Xixi Hu* and Michael Trosset and Minh Tang
Companies: Indiana University Bloomington and Indiana University Bloomington and Johns Hopkins University
Keywords: two-sample test; latent space model; multidimensional scaling; brain networks

Two-sample graph comparison arises in neuroscience, e.g., when comparing the brain networks of people from two populations. Standard practices include tests on aggregated univariate measures, which may not fully characterize a graph, or one test for each edge, which does not exploit graph structure. Assuming a latent space model (LSM) for random graphs, we propose an inferential framework that both compares entire graphs and utilizes underlying structure. Graphs in LSM are described by a set of latent positions and a function that maps distance to edge probability. Our method estimates two sets of common latent positions by multidimensional scaling, then constructs a test statistic by comparing interpoint distance matrices. Simulations show that our test has greater power than either the standard practices or a direct comparison of sample mean graphs. We apply our approach to compare the structural brain networks of (a) autistic vs healthy subjects, and (b) young vs old adults.

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

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