22 citations · 26 across the 13 of their papers we have counts for
15 papers
Linear regression with probabilistic networks for network-linked data
Jingnan Zhang, Shiwen Ye, Danyang Huang
Network-linked data combine a network with covariates and responses observed for its nodes. Common regression approaches either model dependence among neighboring responses using t…
Quantile Social Autoregressive Model
Liyuan Wang, Danyang Huang, Wei Lan +1
Research on modelling peer effects has predominantly relied on linear-in-means models. However, averaging peer responses prevents these models from capturing the effects of extreme…
Pseudo-Likelihood Ratio Screening based on Network Data with Applications
Wei Hu, Danyang Huang, Bo Zhang
Social network platforms today generate vast amounts of data, including network structures and a large number of user-defined tags, which reflect users' interests. The dimensionali…
Distributed Pseudo-Likelihood Method for Community Detection in Large-Scale Networks
Jiayi Deng, Danyang Huang, Bo Zhang
This paper proposes a distributed pseudo-likelihood method (DPL) to conveniently identify the community structure of large-scale networks. Specifically, we first propose a block-wi…
Statistical Inference for Regression with Imputed Binary Covariates with Application to Emotion Recognition
Ziqian Lin, Danyang Huang, Ziyu Xiong +1
In the flourishing live streaming industry, accurate recognition of streamers' emotions has become a critical research focus, with profound implications for audience engagement and…
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
Xuetong Li, Yuan Gao, Hong Chang +11
This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rap…
Bias-Correction for Privacy-Protected Spatial Autoregressive Models with Application to Restaurant Network Analysis
Danyang Huang, Ziyi Kong, Shuyuan Wu +1
Spatial autoregressive (SAR) models and their extensions are important tools for studying network effects. However, with an increasing emphasis on data privacy, data providers ofte…
Mini-batch Gradient Descent with Buffer
Haobo Qi, Du Huang, Yingqiu Zhu +2
In this paper, we studied a buffered mini-batch gradient descent (BMGD) algorithm for training complex model on massive datasets. The algorithm studied here is designed for fast tr…
Quasi-Newton Updating for Large-Scale Distributed Learning
Shuyuan Wu, Danyang Huang, Hansheng Wang
Distributed computing is critically important for modern statistical analysis. Herein, we develop a distributed quasi-Newton (DQN) framework with excellent statistical, computation…
Subsampling-Based Modified Bayesian Information Criterion for Large-Scale Stochastic Block Models
Jiayi Deng, Danyang Huang, Xiangyu Chang +1
Identifying the number of communities is a fundamental problem in community detection, which has received increasing attention recently. However, rapid advances in technology have…
Network Gradient Descent Algorithm for Decentralized Federated Learning
Shuyuan Wu, Danyang Huang, Hansheng Wang
We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it…
A Wasserstein distance-based spectral clustering method for transaction data analysis
Yingqiu Zhu, Danyang Huang, Bo Zhang
With the rapid development of online payment platforms, it is now possible to record massive transaction data. Clustering on transaction data significantly contributes to analyzing…
Subsampling Spectral Clustering for Large-Scale Social Networks
Jiayi Deng, Yi Ding, Yingqiu Zhu +3
Online social network platforms such as Twitter and Sina Weibo have been extremely popular over the past 20 years. Identifying the network community of a social platform is essenti…
Automatic, Dynamic, and Nearly Optimal Learning Rate Specification by Local Quadratic Approximation
Yingqiu Zhu, Yu Chen, Danyang Huang +2
In deep learning tasks, the learning rate determines the update step size in each iteration, which plays a critical role in gradient-based optimization. However, the determination…
Efficient Estimation for Generalized Linear Models on a Distributed System with Nonrandomly Distributed Data
Feifei Wang, Danyang Huang, Yingqiu Zhu +1
Distributed systems have been widely used in practice to accomplish data analysis tasks of huge scales. In this work, we target on the estimation problem of generalized linear mode…