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In this paper, we consider the problem of two-sample testing of large graphs. We demonstrate the practical merits and limitations of existing theoretical tests and their bootstrapped variants. We also propose two new tests based on asymptotic distributions. We show that these tests are computationally less expensive and, in
In this paper, we consider the problem of two-sample testing of large graphs. We demonstrate the practical merits and limitations of existing theoretical tests and their bootstrapped variants. We also propose two new tests based on asymptotic distributions. We show that these tests are computationally less expensive and, in
Two-sample hypothesis testing for random graphs arises naturally in neuroscience, social networks, and machine learning. In this paper, we consider a semiparametric problem of two-sample hypothesis testing for a class of latent position random graphs.
The classical problem of two-sample hypothesis testing is relatively new in the setting of latent position random graphs. Previous research outlines theo-retical and practical applications of two-sample hypothesis testing (Tang et al., 2017, Ghoshdastier et al., 2017, Ginestet et al., 2017).
Abstract. Network (graph) data analysis is a popular research topic in statistics and machine learning. In application, one is frequently confronted with graph two-sample hypothesis testing where the goal is to test the difference be-tween two graph populations.
12 lut 2020 · Examples of applications include the two-sample test problem and the change point detection problem. In the former, we compare two populations of graphs using a distance statistic, and we experimentally test whether both populations could be generated by the same probability distribution.
Walk through deriving a general formula for the distance between two points. The distance between the points ( x 1, y 1) and ( x 2, y 2) is given by the following formula: ( x 2 − x 1) 2 + ( y 2 − y 1) 2. In this article, we're going to derive this formula!
