We prove a Sanov-type large deviation principle for the component empirical measure of certain families of sparse random graphs whose vertices are marked with i.i.d. random variables. Specifically, we show that the rate function can be expressed in a fairly tractable form involving suitable relative entropies. We illustrate two applications of this result: (i) we quantify probabilities of rare events in stochastic networks on sparse random graphs, and (ii) we study Gibbs conditioning principles given suitable rare events associated with the component empirical measure. This talk is based on joint work with I-Hsun Chen, Ivan Lee, and Kavita Ramanan.
Sarath Yasodharan is an Assistant Professor in Industrial Engineering and Operations Research (IEOR) at the Indian Institute of Technology Bombay. He received his PhD from the Indian Institute of Science in 2022 and was a postdoctoral research associate at Brown University from 2022-2024. His research interests are broadly in applied probability.