Heterogeneous Dense Subhypergraph Detection
Machine Learning
2021-04-12 v1 Machine Learning
Statistics Theory
Statistics Theory
Abstract
We study the problem of testing the existence of a heterogeneous dense subhypergraph. The null hypothesis corresponds to a heterogeneous Erd\"{o}s-R\'{e}nyi uniform random hypergraph and the alternative hypothesis corresponds to a heterogeneous uniform random hypergraph that contains a dense subhypergraph. We establish detection boundaries when the edge probabilities are known and construct an asymptotically powerful test for distinguishing the hypotheses. We also construct an adaptive test which does not involve edge probabilities, and hence, is more practically useful.
Cite
@article{arxiv.2104.04047,
title = {Heterogeneous Dense Subhypergraph Detection},
author = {Mingao Yuan and Zuofeng Shang},
journal= {arXiv preprint arXiv:2104.04047},
year = {2021}
}