讲座题目：How many communities are there in a network?

讲座时间：2020/11/4 10:00-12:00

讲座人： 马舒洁 副教授，University of California, Riverside

讲座简介：

Advances in modern technology have facilitated the collection of network data which emerge in many fields including biology, bioinformatics, physics, economics, sociology and so forth. Network data often have natural communities which are groups of interacting objects (i.e., nodes); pairs of nodes in the same group tend to interact more than pairs belonging to different groups. Community detection then becomes a very important task, allowing us to identify and understand the structure of a network. Thus, the development of methods for community detection has attracted much attention in the past decade, and as a result, different efficient approaches have been proposed in literature.

A fundamental limitation of most existing methods is that they divide networks into a fixed number of communities, i.e., the number of communities is known and given in advance. However, in practice, such prior information is typically unavailable. Determining the number of communities is a challenging yet important task, as the following community detection procedure relies upon it. In this talk, I will introduce a convenient and effective solution to this problem under the degree-corrected stochastic block models (DC-SBM). The proposed method takes advantages of spectral clustering, likelihood principle and binary segmentation. Determining the number of communities is essentially a model selection problem, and we therefore establish the selection consistency of our proposed procedure under a mild condition on the average degree. We demonstrate the approach on different networks.

讲座人简介：

        马舒洁 副教授，2011年于密歇根州立大学统计与概率系获得博士学位。现为加州大学河滨分校统计系副教授。 现担任 Journal of the American Statistical Association, Journal of Business & Economic Statistics等多个统计类学术期刊副主编。她目前研究兴趣包括 大规模数据分析，精准医疗，机器学习，网络数据分析以及非参数和半参数推断。先后在统计学和经济学国际学术期刊上发表四十余篇学术论文。

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