Robert Lunde

Modern Statistical Tools for Network Data: From Resampling to Conformal Prediction

Network data, which represent complex relationships between different entities, have become increasingly common in fields ranging from neuroscience to social network analysis. To address key scientific questions in these domains, versatile inferential methods for network-valued data are needed. In this talk, I will discuss network analogs of two modern statistical methods: subsampling and conformal prediction. While network data are generally dependent, we show that these methods exhibit similar properties to their IID counterparts by leveraging structure arising from an exchangeability assumption. I will also discuss related theoretical results, including central limit theorems for eigenvalues. This is joint work with Purnamrita Sarkar, Elizaveta Levina, and Ji Zhu.