Academic Report of School of Mathematical Sciences [2025] No. 111

(Series Report for High-Level University Construction No. 1213)

Title: Dynamic Networks with Node Heterogeneity and Homophily

Speaker: Associate Professor Chiang Bin Yeng (The Hong Kong Polytechnic University)

Time: 16:00-17:00, November 11, 2025 (Tuesday)

Location: Conference Room 303, Alumni Square

Abstract:

Statistical modeling of network data is an important topic in various areas. Although many real networks are dynamic in nature, most existing statistical models and related inferences for network data are confined to static networks, and the development of the foundation for dynamic network models is still in its infancy. To the best of our knowledge, no attempts have been made to jointly address node heterogeneity and link homophily among dynamic networks. Being able to capture these network features simultaneously will not only bring new insights on understanding how networks were formed but also provide more sophisticated tools for the prediction of a future network with statistical guarantees. In particular, our model accounts for link homophily associated with both observed traits and latent traits of the nodes. A novel normalized least squared loss-based framework is constructed to generate stable estimations for the high dimensional parameters. The promising performance of the proposed model is further illustrated by various simulation and real data studies.

Speaker Profile:

Dr. Chiang Bin Yeng received his B.Sc. in Statistics from the University of Science and Technology of China in 2007, and his Ph.D. in Statistics and Applied Probability from the National University of Singapore in 2012. After completing his Ph.D., he conducted postdoctoral research at Carnegie Mellon University. He joined The Hong Kong Polytechnic University in August 2015, where he is currently an Associate Professor and Associate Head in the Department of Data Science and Artificial Intelligence. He serves as an Associate Editor for the Hacettepe Journal of Mathematics and Statistics and IEEE Transactions on Emerging Topics in Computational Intelligence. His primary research area is statistics, with interests including high-dimensional data analysis and network data analysis. His representative work on statistical theory and modeling has been published in leading statistics and machine learning journals such as JRSSB, JASA, Biometrika, Annals of Statistics, and JMLR.

All faculty and students are welcome!

Host: Wang Jiangzhou

School of Mathematical Sciences

November 6, 2025