Sharp detection boundaries on testing dense subhypergraph

We study the problem of testing the existence of a dense subhypergraph. The null hypothesis corresponds to an ErdősRényi uniform random hypergraph and the alternative hypothesis corresponds to a uniform random hypergraph that contains a dense subhypergraph. We establish sharp detection boundaries in both scenarios: (1) the edge probabilities are known; (2) the edge probabilities are unknown. In both scenarios, sharp detection boundaries are characterized by the appropriate model parameters. Asymptotically powerful tests are provided when the model parameters fall in the detectable regions. Our results indicate that the detectable regions for general hypergraph models are dramatically different from their graph counterparts. MSC2020 subject classifications: Primary 62G10; secondary 05C80

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