275 citations · 562 across the 20 of their papers we have counts for
20 papers
Variational Estimators for Node Popularity Models
Jony Karki, Dongzhou Huang, Yunpeng Zhao
Node popularity is recognized as a key factor in modeling real-world networks, capturing heterogeneity in connectivity across communities. This concept is equally important in bipa…
Network Inference Using the Hub Model and Variants
Zhibing He, Yunpeng Zhao, Peter Bickel +3
Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the…
Multiple Testing of Local Extrema for Detection of Structural Breaks in Piecewise Linear Models
Zhibing He, Dan Cheng, Yunpeng Zhao
In this paper, we propose a new generic method for detecting the number and locations of structural breaks or change points in piecewise linear models under stationary Gaussian noi…
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks
Yunpeng Zhao, Ning Hao, Ji Zhu
Bipartite graphs are ubiquitous across various scientific and engineering fields. Simultaneously grouping the two types of nodes in a bipartite graph via biclustering represents a…
An Effective Bernstein-type Bound on Shannon Entropy over Countably Infinite Alphabets
Yunpeng Zhao
We prove a Bernstein-type bound for the difference between the average of negative log-likelihoods of independent discrete random variables and the Shannon entropy, both defined on…
An Optimal Uniform Concentration Inequality for Discrete Entropies on Finite Alphabets in the High-dimensional Setting
Yunpeng Zhao
We prove an exponential decay concentration inequality to bound the tail probability of the difference between the log-likelihood of discrete random variables on a finite alphabet…
Identifiability and consistency of network inference using the hub model and variants
Yunpeng Zhao, Peter Bickel, Charles Weko
Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the…
A Note on New Bernstein-type Inequalities for the Log-likelihood Function of Bernoulli Variables
Yunpeng Zhao
We prove a new Bernstein-type inequality for the log-likelihood function of Bernoulli variables. In contrast to classical Bernstein's inequality and Hoeffding's inequality when app…
Logistic Regression Augmented Community Detection for Network Data with Application in Identifying Autism-Related Gene Pathways
Yunpeng Zhao, Qing Pan, Chengan Du
When searching for gene pathways leading to specific disease outcomes, additional information on gene characteristics is often available that may facilitate to differentiate genes…
Penalized Component Hub Models
Charles Weko, Yunpeng Zhao
Social network analysis presupposes that observed social behavior is influenced by an unobserved network. Traditional approaches to inferring the latent network use pairwise descri…
A Survey on Theoretical Advances of Community Detection in Networks
Yunpeng Zhao
Real-world networks usually have community structure, that is, nodes are grouped into densely connected communities. Community detection is one of the most popular and best-studied…
Network Inference from Temporal-Dependent Grouped Observations
Yunpeng Zhao
In social network analysis, the observed data is usually some social behavior, such as the formation of groups, rather than an explicit network structure. Zhao and Weko (2017) prop…
On Consistency of Graph-based Semi-supervised Learning
Chengan Du, Yunpeng Zhao, Feng Wang
Graph-based semi-supervised learning is one of the most popular methods in machine learning. Some of its theoretical properties such as bounds for the generalization error and the…
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…
Network Inference from Grouped Data
Yunpeng Zhao, Charles Weko
In medical research, economics, and the social sciences data frequently appear as subsets of a set of objects. Over the past century a number of descriptive statistics have been de…
Network induced large correlation matrix estimation
Shuo Chen, Jian Kang, Yishi Xing +2
The correlation matrix of massive biomedical data (e.g. gene expression or neuroimaging data) often exhibits a complex and organized, yet latent graph topological structure. We pro…
Asymptotic normality in the maximum entropy models on graphs with an increasing number of parameters
Ting Yan, Yunpeng Zhao, Hong Qin
Maximum entropy models, motivated by applications in neuron science, are natural generalizations of the -model to weighted graphs. Similar to the -model, each vertex in maxim…
Link prediction for partially observed networks
Yunpeng Zhao, Elizaveta Levina, Ji Zhu
Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative example…
Consistency of community detection in networks under degree-corrected stochastic block models
Yunpeng Zhao, Elizaveta Levina, Ji Zhu
Community detection is a fundamental problem in network analysis, with applications in many diverse areas. The stochastic block model is a common tool for model-based community det…
Community extraction for social networks
Yunpeng Zhao, Elizaveta Levina, Ji Zhu
Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields, with applications ranging from citation and friend…