Speaker: 王天栋 (复旦大学上海数学中心)
Time: 2025-10-28 10:30-11:30
Location: 1210, SIMIS
Abstract: In this talk, we introduce a multilayer inhomogeneous random graph model (MIRG). Layers of the MIRG may consist of both single-edge and multi-edge graphs. In the single layer case, it has been shown that the regular variation of the weight distribution underlying the inhomogeneous random graph implies the regular variation of the typical degree distribution. We extend this correspondence to the multilayer case by showing that multivariate regular variation of the weight distribution implies multivariate regular variation of the asymptotic degree distribution. Furthermore, under suitable assumptions, the extremal dependence structure present in the weight distribution will be adopted by the asymptotic degree distribution. By considering the asymptotic degree distribution, a wider class of Chung-Lu and Norros-Reittu graphs may be incorporated into the MIRG layers. Additionally, we prove consistency of the Hill estimator when applied to degrees of the MIRG that have a tail index greater than 1. Simulation results indicate that, in practice, hidden regular variation may be consistently detected from an observed MIRG. Finally, we analyze user interactions on Reddit and observe that they exhibit properties of the MIRG.
About Speaker:
Tiandong Wang is a Young Investigator (Tenure-Track Associate Professor) at the Shanghai Center for Mathematical Sciences, Fudan University, a position she has held since September 2022. Her research focuses on the intersection of applied probability and statistics, with an emphasis on modeling heavy-tailed phenomena in complex networks. Dr. Wang earned her Ph.D. in Operations Research from the School of Operations Research and Information Engineering, Cornell University in August 2019, where she was advised by Prof. Sidney Resnick. Prior to joining Fudan University, she served as an Assistant Professor in the Department of Statistics at Texas A&M University from September 2019 to August 2022. Her work bridges statistical methodology and real-world applications, addressing challenges in network analysis, extreme value theory, and data science.
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