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Liyuan Scholars Colloquium Session 174: Community detection in networks under weighted popularity-adjusted block models

Time:2026-06-11 09:51

主讲人 Binghui Liu 讲座时间 15:00–16:00, June 26, 2026
讲座地点 Room 1, Huixing Building, Yuehai Campus, Shenzhen University 实际会议时间日 26
实际会议时间年月 2026.6

Shenzhen University School of Mathematical Sciences  

Liyuan Scholars Colloquium Session 174


Title: Community detection in networks under weighted popularity-adjusted block models

Speaker: Professor Binghui Liu  (Northeast Normal University )

Time: 15:00–16:00, June 26, 2026

Location: Room 1, Huixing Building, Yuehai Campus, Shenzhen University

Abstract:  In this paper, we address the problem of community detection in networks under the weighted popularity-adjusted block model (wPABM). This model extends conventional block models by introducing node-specific “popularity” parameters that capture interaction effects between communities and nodes, while also incorporating weighted edges. Notably, without restricting the edge weights to any pre-specified distribution, we only assume that their distributions belong to the family of non-negative sub-exponential family. To fit the wPABM, we develop a computationally efficient method called the weighted popularity-adjusted profile-pseudo-likelihood (wPAPPL) method and establish the consistency of the resulting parameter estimators. Extensive simulations and a real-data analysis demonstrate that the proposed method achieves highly competitive and robust community detection performance across diverse data and distribution types, with very reasonable computational cost.


Speaker Profile: Binghui Liu is a professor and associate dean at the School of Mathematics and Statistics, Northeast Normal University, and director of the Jilin Provincial Key Laboratory of Big Data and Intelligent Analysis. He has been selected as a National-Level Young Talent, an Outstanding Young Scholar at the National Tianyuan Mathematics Northeast Center, and a Top Innovative Talent in Jilin Province. His research focuses primarily on statistical machine learning and network data analysis. He has published over 40 academic papers in journals across the fields of statistics, computer science & artificial intelligence, and econometrics, with some of his work appearing in leading journals such as JASA, AoS, AoAS, AIJ, JMLR, JoE, and JBES; he serves as Vice President of the Causal Inference Branch of the Chinese Society of Field Statistics and Vice President of the Interdisciplinary Statistical Research Branch of the Chinese Society of Field Statistics.



All faculty and students are welcome!  


School of Mathematical Sciences  

June 10, 2026