A Semiparametric Two-Sample Hypothesis Testing Problem for Random Graphs

作者：Tang Minh^*; Athreya Avanti; Sussman Daniel L; Lyzinski Vince; Park Youngser; Priebe Carey E

来源：Journal of Computational and Graphical Statistics, 2017, 26(2): 344-354.

DOI：10.1080/10618600.2016.1193505

• Random dot product graph
• Semiparametric graph inference
• Two-sample hypothesis testing

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.

• 出版日期2017