5 citations · 11 across the 9 of their papers we have counts for
9 papers
From Simple to Composite Perturbations: A Unified Decomposition Framework for Stochastic Block Models
Jianwei Hu, Ding Chen, Ji Zhu
Statistical inference for stochastic block models typically relies on the spectrum of the normalized adjacency matrix $\A^*$. In practice, the true probability matrix …
Extreme eigenvalues of principal minors of random matrix with moment conditions
Jianwei Hu, Seydou Keita, Kang Fu
Let be a random sample of size from a -dimensional population distribution, where . Consider a symmetric matrix $W=X^\top…
Two-Sample Test for Stochastic Block Models via the Largest Singular Value
Kang Fu, Jianwei Hu, Seydou Keita +1
The stochastic block model is widely used for detecting community structures in network data. However, the research interest of much literature focuses on the study of one sample o…
Two-Sample Test for Stochastic Block Models via Maximum Entry-wise Deviation
Kang Fu, Jianwei Hu, Seydou Keita +1
The stochastic block model is a popular tool for detecting community structures in network data. Detecting the difference between two community structures is an important issue for…
Differentially private estimation in a class of directed network models
Lu Pan, Jianwei Hu, Peiyan Li
Although the theoretical properties in the model based on a differentially private bi-degree sequence have been derived, it is still lack of a unified theory for a general cl…
Limiting laws and consistent estimation criteria for fixed and diverging number of spiked eigenvalues
Jianwei Hu, Jingfei Zhang, Jianhua Guo +1
In this paper, we study limiting laws and consistent estimation criteria for the extreme eigenvalues in a spiked covariance model of dimension . Firstly, for fixed , we propo…
Detection of the number of principal components by extended AIC-type method
Jianwei Hu, Jingfei Zhang, Ji Zhu
Estimating the number of principal components is one of the fundamental problems in many scientific fields such as signal processing (or the spiked covariance model). In this paper…
Using Maximum Entry-Wise Deviation to Test the Goodness-of-Fit for Stochastic Block Models
Jianwei Hu, Jingfei Zhang, Hong Qin +2
The stochastic block model is widely used for detecting community structures in network data. How to test the goodness-of-fit of the model is one of the fundamental problems and ha…
Corrected Bayesian information criterion for stochastic block models
Jianwei Hu, Hong Qin, Ting Yan +1
Estimating the number of communities is one of the fundamental problems in community detection. We re-examine the Bayesian paradigm for stochastic block models and propose a "corre…