- Zhou, T.,Pan, R.#, Zhang, J.*, and Wang, H. (2025), “An attribute-based Node2Vec model for dynamic community detection in co-authorship network,” Computational Statistics, 40,177-204. 
- Gao, Y.,Pan, R.*, Li, F., Zhang, R. and Wang, H. (2024), “Grid point approximation for distributed nonparametric smoothing and prediction”, Journal of Computational and Graphical Statistics, accepted. 
- Zhang, Y.,Pan, R.*, Zhu, X., Fang, K. * and Wang, H. (2024), “A latent space model for weighted keyword co-occurrence networks with applications in knowledge discovery in statistics”, Journal of Computational and Graphical Statistics, accepted. 
- Li, X., Gao, Y.*, Chang, H., Huang, D., Ma, Y.,Pan, R., et al. (2024), “A selective review on statistical methods for massive data computation: distributed computing, subsampling, and minibatch techniques,” Statistical Theory and Related Fields, 8(3), 163—185. 
- Pan, R.,Liu, T., and Ma, L. (2024), “A Graph Attention Recurrent Neural Network Model for PM2.5 Prediction: A Case Study in China from 2015 to 2022”, Atmosphere, 15, 799. 
- Guo, B., Wang, L.,Pan, R.*,and Zhu, X. (2024), “A grouped spatial-temporal model for PM2.5 data and its applications on outlier detection,” Communications in Statistics – Simulation and Computation, 53(5), 2565—2577. 
- Gao, T.,Pan, R.,Zhang, J.*, and Wang, H. (2024), “Community detection in temporal citation network via a tensor-based approach,” Statistics and Its Interface, 17(2), 145—158. 
- Gao, T., Liu, J.,Pan, R.*,and Wang, H. (2024), “Citation counts prediction of statistical publications based on multi-layer academic networks via neural network model,” Expert Systems with Applications, 238, 121634. 
- Ding,Y.,Pan, R.*, Zhang, Y., and Zhang, B. (2023), “A matrix completion bootstrap method for estimating scale-free network degree distribution,” Knowledge-Based Systems,277,110803. 
- Pan, R., Zhu, Y.*, Guo, B., Zhu, X., and Wang, H. (2023), “A sequential addressing subsampling method for massive data analysis under memory constraint,” IEEE Transactions on Knowledge and Data Engineering, 35(9), 9502-9513. 
- Zhang, Y.,Pan, R.*, Wang, H., and Su, H. (2023), “Community Detection in Attributed Collaboration Network for Statisticians,” Stat, 12(1), e507. 
- Pan, R., Ren, T.*, Guo, B., Li, F., Li, G., and Wang, H. (2022), “A note on distributed quantile regression by pilot sampling and one-step updating,” Journal of Business and Economics Statistics, 40(4), 1691—1700. 
- Zhu, X., Wu, S.*,Pan, R., and Wang, H. (2022), “Feature screening for massive data analysis by subsampling,” Journal of Business and Economics Statistics, 40(4), 1892—1903. 
- Song, X., Zhang, Y.*,Pan, R.*, and Wang, H. (2022), “Link prediction for statistical collaboration networks incorporating institutes and research interests,” IEEE Access, 10,104954—104965. 
- Pan, R., Chang, X.*, Zhu, X., and Wang, H. (2022), “Link prediction via latent space logistic regression model,” Statistics and Its Interface, 15(3), 267—282. 
- Gao, T., Zhang, Y., Wang, S., Yang, Y., and Pan, R.*(2021), “Community Detection for Statistical Citation Network by D-SCORE,” Statistics and Its Interface, 14(3), 279—294. 
- Zhu, X.,Pan, R.*, Zhang, Y., Chen, Y.,Mi, W.,and Wang, H. (2021), “Information Diffusion withNetworkStructures,”Statistics and Its Interface,14(2), 115—129. 
- Zhu, X.,Huang, D.*,Pan, R., and Wang, H. (2020), “Multivariate Spatial Autoregressive Modelfor Large Scale Social Networks,”Journal of Econometrics,215(2), 591—606. 
- Zhu, X., and Pan, R.*(2020),“Grouped Network Vector Autoregression,”Statistica Sinica,30(3), 1437—1462. 
- Ma, Y.,Pan, R.*, Zou, T., and Wang, H. (2020), “A Naive Least Squares Method for Spatial Autoregression with Covariates,” Statistica Sinica,30(2), 653—672. 
- Zhang, X.,Pan, R., Guan, G.*, Zhu, X., and Wang, H. (2020), “Logistic Regressionwith Network Structure,” Statistica Sinica,30(2), 673—693. 
- Zhou, J., Li, D.*,Pan, R., and Wang, H. (2020),“Network GARCH Model,”Statistica Sinica,30(3),1723—1740. 
- Cheng, H., Li, S., Ning, Y., Chen, X.,Pan, R., and Zhang, Z. (2020), “Analysis on utilization of Beijing local roads using taxi GPS data,” Physica A, 545, 123570. 
- Xu, K., Wang, J.*,Pan, R., and Wang, H. (2019),“Photographic Diary: A New Estimation Approach to PM2.5 Monitoring,”Statistics and Its Interface,12, 387—395. 
- Zhang, Y., Fan, J.,Pan, R.*, and Huang, L. (2019),“Usage Based Insurance with pointof interestdata,”Statistics and Its Interface, 12, 345—353. 
- Chen, Y.,Pan, R.*, Guan, R., and Wang, H. (2019),“A case study for Beijing Point of Interest Data Using Group Linked Cox Process,”Statistics and Its Interface, 12, 331—344. 
- Cai, W., Guan, G.,Pan, R.*, Zhu, X., and Wang, H. (2018), “Network Linear Discriminant Analysis,” Computational Statistics and Data Analysis, 117, 32—44. 
- Pan, R., Guan, R.*, Zhu, X., and Wang, H. (2018), “A Latent Moving Average Model for Network Regression,”Statistics and Its Interface, 11(4), 641—648. 
- Zhu, X.,Pan, R.*, Li, G., Liu, Y., and Wang, H. (2017), “Network Vector Autoregression,” Annals of Statistics, 45(3), 1096—1123. 
- Lan, W.,Pan, R., Luo, R.*, and Cheng Y. (2017),“High Dimensional Cross-Sectional Dependence Test under Arbitrary Serial Correlation,”Science China: Mathematics, 60, 345—360. 
- Pan, R., Wang, H.*, and Li, R. (2016),“Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening,”Journal of the American Statistical Association, 111(513), 169--179. 
- Zhu, X., Huang, D.*,Pan, R., and Wang, H. (2016),“An EM algorithm for click fraud detection,”Statistics and Its Interface, 9, 389-394. 
- Pan, R.*, and Wang, H. (2015),“A Note on Testing Conditional Independence for Social Network Analysis,”SCIENCE CHINA: Mathematics, 58(6), 1179-1190. 
- Pan,R., Wang, H.*, and Tsai, C. (2011),“Regression Analysis of Asymmetric Pairs in Large-Scale Network Data,”Communications in Statistics: Simulation and Computation, 40:10, 1540-1547. 
- Li, J., Pan, R., and Wang, H. (2010),“Selection of Best Keywords: A Poisson Regression Model,”Journal of Interactive Advertising, 11(1), 27-35. 
- 高天辰, 张妍, 潘蕊(2024). 统计学科大规模多层学术网络数据集——网络构建、描述分析与实际应用. 经济管理学刊, 3(4),237—260. 
- 张妍,潘蕊,方匡南(2023),基于合作者网络社区发现的学科主题分析—以国际统计学期刊为例,经济管理学刊,2(2),219—240. 
- 王菲菲,朱雪宁*,潘蕊(2021),广义网络向量自回归,中国科学:数学,51(8),1253--1266. 
- 潘蕊,周静*,关蓉(2017),“网络中意见领袖对客户间接价值的影响,” 《商业研究》,59(9),28—32.