【主题】Determining the number of communities in degree-corrected stochastic block models

【报告人】Ma Shujie , 副教授

University of California, Riverside

【时间】 2019年7月1日（星期一）09:00-10:00

【地点】上海财经大学统计与管理学院大楼1208会议室

【摘要】We propose to estimate the number of communities in degree-corrected stochastic block models based on a pseudo likelihood ratio. For estimation, we consider a spectral clustering together with binary segmentation method. This approach guarantees an upper bound for the pseudo likelihood ratio statistic when the model is over-fitted. We also derive its limiting distribution when the model is under-fitted. Based on these properties, we establish the consistency of our estimator for the true number of communities. Developing these theoretical properties require a mild condition on the average degree -- growing at a rate of log(n), where n is the number of nodes. Our proposed method is further illustrated by simulation studies and analysis of real-world networks. The numerical results show that our approach has satisfactory performance when the network is sparse and/or has unbalanced communities.

【嘉宾简介】马舒洁教授于2011年在美国密西根州立大学获得统计学博士学位。现为美国加州大学河滨分校终身教授、副教授。担任Journal of Business & Economic Statistics 和Statistica Sinica 等多个统计类国际学术期刊的副主编（Associate Editor）。她目前的主要研究方向包括大规模数据分析，精准医疗，高维数据分析，纵向数据分析及在环境风险估测，营养学，医药学和金融学的统计方法应用。先后在统计学和经济学国际期刊上发表三十余篇学术论文。

【主持人】冯兴东