activity
20172022
most citedA Sharp Lower Bound for Mixed-membership Estimation

10 citations · 20 across the 9 of their papers we have counts for

collaborators

13 papers

cs.DL20223 cited

Co-citation and Co-authorship Networks of Statisticians

Pengsheng Ji, Jiashun Jin, Zheng Tracy Ke +1

We collected and cleaned a large data set on publications in statistics. The data set consists of the coauthor relationships and citation relationships of 83, 331 papers published…

math.ST20223 cited

Power Enhancement and Phase Transitions for Global Testing of the Mixed Membership Stochastic Block Model

Louis Cammarata, Zheng Tracy Ke

The mixed-membership stochastic block model (MMSBM) is a common model for social networks. Given an -node symmetric network generated from a -community MMSBM, we would like t…

cs.SI2022

The SCORE normalization, especially for highly heterogeneous network and text data

Zheng Tracy Ke, Jiashun Jin

SCORE was introduced as a spectral approach to network community detection. Since many networks have severe degree heterogeneity, the ordinary spectral clustering (OSC) approach to…

stat.ML2020

Measurement error models: from nonparametric methods to deep neural networks

Zhirui Hu, Zheng Tracy Ke, Jun S Liu

The success of deep learning has inspired recent interests in applying neural networks in statistical inference. In this paper, we investigate the use of deep neural networks for n…

stat.ME2020

Estimation of the number of spiked eigenvalues in a covariance matrix by bulk eigenvalue matching analysis

Zheng Tracy Ke, Yucong Ma, Xihong Lin

The spiked covariance model has gained increasing popularity in high-dimensional data analysis. A fundamental problem is determination of the number of spiked eigenvalues, . For…

stat.ME2019

Community Detection for Hypergraph Networks via Regularized Tensor Power Iteration

Zheng Tracy Ke, Feng Shi, Dong Xia

To date, social network analysis has been largely focused on pairwise interactions. The study of higher-order interactions, via a hypergraph network, brings in new insights. We stu…