主题：Moment-integrated Bias-adjusted Spectral Method for Community Detection in Multi-layer Networks
主讲人：李高荣 北京师范大学
主持人：刘一鸣 暨南大学
时间：2026年4月22日（周三）下午15:30-16:30
地点：暨南大学石牌校区经济学院大楼（中惠楼）503室
摘要
To detect the global community structure of multi-layer networks, individual layers might not provide sufficient information. It is of vital importance to effectively complement the information from the network data. In this paper, under the framework of multi-layer stochastic block model, a Spectral method with Moments integration and Bias Adjustment (SpecMBA) is proposed for community detection. The key distinguishing feature of SpecMBA is the adaptive integration of both the first and second moments of adjacency matrices with a hyperparameter a∈[−1,∞), which overcomes the limitations of fixed-form aggregation. Furthermore, SpecMBA adjusts for the bias caused by noise heteroskedasticity to mitigate signal distortion. In addition, a data-driven likelihood-based approach is proposed to select the optimal a. This adaptive aggregation ensures robust and competitive performance across diverse scenarios, which has been confirmed in the numerical studies. Under mild conditions, the community detection consistency for SpecMBA is established. The application on the international food trading network reveals interesting findings.
主讲人简介
李高荣，北京师范大学统计学院教授，博士生导师，北京师范大学第十二届“最受本科生欢迎的十佳教师”。主要研究方向是非参数统计、高维统计、统计学习、纵向数据、测量误差数据和因果推断等。迄今为止，在Annals of Statistics, Journal of the American Statistical Association, Journal of Business & Economic Statistics, Statistics and Computing, 《中国科学：数学》和《统计研究》等学术期刊上发表学术论文130余篇。出版5部著作：《纵向数据半参数模型》、《现代测量误差模型》(入选“现代数学基础丛书”系列)、《多元统计分析》(2023年荣获北京高校优质本科教材奖、2026年入选“十四五”普通高等教育本科国家级规划教材）和《统计学习（R语言版）》 (2025年荣获北京高校优质本科教材奖）、《高维统计学》。2024年荣获北京市普通高校优秀本科毕业论文优秀指导教师，2025年荣获北京师范大学高等教育教学成果一等奖。主持国家自然科学基金、北京市自然科学基金和北京市教委科技计划面上项目等国家和省部级科研项目10多项。
校对|刘一鸣
责编| 彭毅
初审| 姜云卢
终审发布| 何凌云
（来源：暨南大学经济学院微信公众号）
