A Semiparametric Two-Sample Hypothesis Testing Problem for Random Graphs. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Tang, Minh; Athreya, Avanti; Sussman, Daniel L.; Lyzinski, Vince; Park, Youngser; Priebe, Carey E.
• Is Part Of: Journal of computational and graphical statistics. Volume 26:Issue 2(2017)
• Page Start: 344
• Page End: 354
• Publication Date: 2017-04-03
• Subjects: Random dot product graph -- Semiparametric graph inference -- Two-sample hypothesis testing; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2016.1193505
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: Two-sample hypothesis testing for random graphs arises naturally in neuroscience, social networks, and machine learning. In this article, we consider a semiparametric problem of two-sample hypothesis testing for a class of latent position random graphs. We formulate a notion of consistency in this context and propose a valid test for the hypothesis that two finite-dimensional random dot product graphs on a common vertex set have the same generating latent positions or have generating latent positions that are scaled or diagonal transformations of one another. Our test statistic is a function of a spectral decomposition of the adjacency matrix for each graph and our test procedure is consistent across a broad range of alternatives. We apply our test procedure to real biological data: in a test-retest dataset of neural connectome graphs, we are able to distinguish between scans from different subjects; and in the C. elegans connectome, we are able to distinguish between chemical and electrical networks. The latter example is a concrete demonstration that our test can have power even for small-sample sizes. We conclude by discussing the relationship between our test procedure and generalized likelihood ratio tests. Supplementary materials for this article are available online.