Abstract: The community detection problem has attracted significant attention in re-cent years, and it has been studied extensively under the framework of a Stochas-tic Block Model (SBM). However, it is well-known that SBMs t real data very poorly, and various extensions have been suggested to replicate characteristics of real data. The recovered community assignments are often sensitive to the model used, and this naturally begs the following question: Given a network with community structure, how to decide whether to t a vanilla SBM, or a more complicated model ? In this talk, we will formulate this problem as a classical goodness of fit question, and try to provide some principled answers in this direction.
This is based on joint work with Rajarshi Mukherjee.
Biography: Subhabrata Sen is Schramm Postdoctoral Fellow at Microsoft Re-search NE and MIT Mathematics. He graduated from the Stanford Statistics Department in 2017, where he was advised by Amir Dembo and Andrea Mon-
tanari. He was awarded the Probability Dissertation Award” for his thesis on Random graphs, optimization, and spin glasses”. His research interests include hypothesis testing and non-parametric inference on one hand, and combinatorial optimization and random graphs on the other.