Abstract
In this talk, I will discuss several different data structures and techniques that allow dynamic graph algorithms to be efficient in more than one practical model of computation. Specifically, I will describe several models of computation that are of practical interest to the dynamic algorithms community including the shared-memory work-depth model, the MPC model, and differential privacy. I will describe in detail specific data structures that are used to solve k-core decomposition, densest subgraph, triangle counting, and other "local" graph problems and how certain characteristics of these data structures allow them to be efficient in a variety of models.