• Bernoulli
  • Volume 23, Number 3 (2017), 1599-1630.

A nonparametric two-sample hypothesis testing problem for random graphs

Minh Tang, Avanti Athreya, Daniel L. Sussman, Vince Lyzinski, and Carey E. Priebe

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We consider the problem of testing whether two independent finite-dimensional random dot product graphs have generating latent positions that are drawn from the same distribution, or distributions that are related via scaling or projection. We propose a test statistic that is a kernel-based function of the estimated latent positions obtained from the adjacency spectral embedding for each graph. We show that our test statistic using the estimated latent positions converges to the test statistic obtained using the true but unknown latent positions and hence that our proposed test procedure is consistent across a broad range of alternatives. Our proof of consistency hinges upon a novel concentration inequality for the suprema of an empirical process in the estimated latent positions setting.

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Bernoulli, Volume 23, Number 3 (2017), 1599-1630.

Received: July 2015
Revised: November 2015
First available in Project Euclid: 17 March 2017

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empirical process nonparametric graph inference random dot product graph


Tang, Minh; Athreya, Avanti; Sussman, Daniel L.; Lyzinski, Vince; Priebe, Carey E. A nonparametric two-sample hypothesis testing problem for random graphs. Bernoulli 23 (2017), no. 3, 1599--1630. doi:10.3150/15-BEJ789.

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