A0780
Title: Lost in the shuffle: Testing power in the presence of errorful network vertex labels
Authors: Vince Lyzinski - University of Maryland, College Park (United States) [presenting]
Ayushi Saxena - University of Maryland College Park (United States)
Abstract: Many two-sample network hypothesis testing methodologies operate under the implicit assumption that the vertex correspondence across networks is a priori known. Power degradation in two-sample graph hypothesis testing is considered when there are misaligned/label-shuffled vertices across networks. In the context of random dot product and stochastic block model networks, the power loss due is theoretically explored to shuffling for a pair of hypothesis tests based on Frobenius norm differences between estimated edge probability matrices or between adjacency matrices. The loss in testing power is further reinforced by numerous simulations and experiments, both in the stochastic block model and in the random dot product graph model, where the power loss is compared across multiple recently proposed tests in the literature. Lastly, the impact that shuffling can have in real-data testing is demonstrated in a pair of examples from neuroscience and social network analysis.
