Nov 17- 2022 SDSA Seminars, Sayan Chakrabarty : Subsampling Based Model Selection in Networks
Channel: Statistics Doctoral Students Association
Date: 17/11/2022

Sayan Chakrabarty, Doctoral Candidate Department of Statistics: Subsampling Based Model Selection in Networks

Abstract:

Complex networks are increasingly prevalent in many scientific applications. Statistical analysis of suck networks depends heavily on the model generating the network. In this project, we develop a subsampling based cross-validation algorithm for model selection in networks. Given a set of candidate models, the algorithm splits the network into multiple subnetworks with a common overlap, fits each candidate model to each subnetwork, and cross-validates the fitted models on the edges between the subnetworks using an appropriate loss function. We apply this algorithm to detect the number of communities in a blockmodel, and to estimate the rank of a random dot product graph model. This method accurately determines the correct model in various scenarios and is significantly faster than the existing methods, making it feasible for model selection in very large networks as well.

https://sdsa.web.illinois.edu/calendar/research-seminar-sayan-chakrabarty-department-of-statistics-and-yun-yang-department-of-statistics/