22 citations · 22 across the 6 of their papers we have counts for
7 papers
Popularity Regression-Based Latent Space Model for Large-Scale Sparse Network Analysis
Zhan Gao, Danyang Huang, Rui Pan +1
Degree heterogeneity is one of the most important properties of network data. It is widely observed that degree heterogeneity is often related to the nodal features. In this study,…
An LLM-Powered Semantic Alignment Framework for Journal Recommendation
Yanglin Yan, Zicheng Xie, Tianchen Gao +2
Journal recommendation is an important task in scholarly information systems. Existing approaches typically rely on supervised learning models, manually engineered features, or his…
Are Large Language Models able to Predict Highly Cited Papers? Evidence from Statistical Publications
Zhanshuo Ye, Yiming Hou, Rui Pan +2
Predicting highly-cited papers is a long-standing challenge due to the complex interactions of research content, scholarly communities, and temporal dynamics. Recent advances in la…
Academic Literature Recommendation in Large-scale Citation Networks Enhanced by Large Language Models
Kun Liu, Yan Zhang, Rui Pan +2
Literature recommendation is essential for researchers to find relevant articles in an ever-growing academic field. However, traditional methods often struggle due to data limitati…
Grid Point Approximation for Distributed Nonparametric Smoothing and Prediction
Yuan Gao, Rui Pan, Feng Li +2
Kernel smoothing is a widely used nonparametric method in modern statistical analysis. The problem of efficiently conducting kernel smoothing for a massive dataset on a distributed…
A Latent Factor Model for High-Dimensional Binary Data
Jiaxin Shi, Yuan Gao, Rui Pan +1
In this study, we develop a latent factor model for analysing high-dimensional binary data. Specifically, a standard probit model is used to describe the regression relationship be…
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…