no code implementations • 11 Feb 2022 • Mingao Yuan, Zuofeng Shang
In this paper, we consider the hypothesis testing of correlation between two $m$-uniform hypergraphs on $n$ unlabelled nodes.
no code implementations • 4 Nov 2021 • Mingao Yuan, Bin Zhao, Xiaofeng Zhao
In practice, a network may has censored (or missing) values and it is shown that censored values have non-negligible effect on the structural properties of a network.
no code implementations • 21 May 2021 • Ruiqi Liu, Mingao Yuan, Zuofeng Shang
Stochastic gradient descent (SGD) and projected stochastic gradient descent (PSGD) are scalable algorithms to compute model parameters in unconstrained and constrained optimization problems.
no code implementations • 5 May 2021 • Mingao Yuan, Zuofeng Shang
We consider the problem of recovering a subhypergraph based on an observed adjacency tensor corresponding to a uniform hypergraph.
no code implementations • 8 Apr 2021 • Mingao Yuan, Zuofeng Shang
We study the problem of testing the existence of a heterogeneous dense subhypergraph.
no code implementations • 29 Jan 2021 • Mingao Yuan, Qian Wen
In this paper, we study the weighted graph two-sample hypothesis testing problem and propose a practical test statistic.
Methodology Applications
no code implementations • 15 Jan 2021 • Mingao Yuan, Qian Wen
However, the computational complexity of the scan test is generally not polynomial in the graph size, which makes the test impractical for large or moderate networks.
no code implementations • 12 Jan 2021 • Mingao Yuan, Zuofeng Shang
In both scenarios, sharp detectable boundaries are characterized by the appropriate model parameters.
no code implementations • 12 Jul 2018 • Mingao Yuan, Yang Feng, Zuofeng Shang
A fundamental problem in network data analysis is to test Erd\"{o}s-R\'{e}nyi model $\mathcal{G}\left(n,\frac{a+b}{2n}\right)$ versus a bisection stochastic block model $\mathcal{G}\left(n,\frac{a}{n},\frac{b}{n}\right)$, where $a, b>0$ are constants that represent the expected degrees of the graphs and $n$ denotes the number of nodes.