Stochastic network dynamics

In this project, we study dynamical processes that evolve on networks and whose evolution is determined by the network structure. Examples include the spread of epidemic diseases in populations, decision-making mechanisms, and opinion dynamics in social communities. A common feature that characterizes these three examples (and many other real-world network dynamics) is the presence of uncertainties, bounded rationality of human behavior, and noise, which can be captured by a stochastic formalization of the dynamical process. Hence, in this project, we mostly focus on the modeling of stochastic network dynamics, their rigorous analysis, and the use of the insight gained through the analysis to design effective strategies to control the system's evolution.