English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Many two-sample network hypothesis testing methodologies operate under theimplicit assumption that the vertex correspondence across networks is a prioriknown. In this paper, we consider the degradation of power in two-sample graphhypothesis testing when there are misaligned/label-shuffled vertices acrossnetworks. In the context of random dot product and stochastic block modelnetworks, we theoretically explore the power loss due to shuffling for a pairof hypothesis tests based on Frobenius norm differences between estimated edgeprobability matrices or between adjacency matrices. The loss in testing poweris further reinforced by numerous simulations and experiments, both in thestochastic block model and in the random dot product graph model, where wecompare the power loss across multiple recently proposed tests in theliterature. Lastly, we demonstrate the impact that shuffling can have inreal-data testing in a pair of examples from neuroscience and from socialnetwork analysis.

Lost in the Shuffle: Testing Power in the Presence of Errorful Network Vertex Labels