- Broida 1640
Title: How many communities are there in a network?
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. At the end of my talk, I will briefly talk about our other on-going and future research projects in this line of work.
Dr. Shujie Ma is an associate professor in Department of Statistics at UC-Riverside. Her current methodological research focuses on developing cutting-edge nonparametric and semiparametric machine learning methods and state-of-art algorithms for dimension reduction, modeling and inference of modern massive data, such as large-scale observation data, network data, large-dimensional time series and genetic data. The applications of her research include gene-environment interactions, environmental risk assessment on child growth, treatment selection, and financial and social network data. Her research has been supported by NSF and NIH. She also received a Hellman Fellowship on methodological developments for environmental risk assessment. She is serving as associate editors for several statistical journals including American Statistician, Computational Statistics and Data Analysis, Journal of Business & Economic Statistics, Journal of Statistical Planning and Inference, and Statistica Sinica.