Zoom link: https://princeton.zoom.us/j/91006932581?pwd=bjd6OGg3TXdGWXIybFB0Nk53TmdWQT09

Correlated random graph models have received much attention recently due to their relevance to various applications, for instance, understanding the graph matching problem in an average-case setting. In this talk, I will discuss models of correlated randomly growing graphs. I will focus on the fundamental statistical questions of detecting correlation and estimating aspects of the correlated structure. Our results highlight the influence of the seed graph in the underlying growth model and its connections with these detection and estimation questions. This is based on joint work with Anirudh Sridhar.