Sparse Resource Allocation for Spreading Processes on Temporal-Switching Networks

Abstract

Spreading processes, e.g. epidemics and rumors, are often modeled on static networks. However, their underlying network structures, such as changing contacts in social networks, are, due to ever-changing circumstances, inherently time-varying in nature. In this paper, we propose an optimization framework for sparse resource allocation for control of spreading processes over temporal networks with known connectivity patterns. We use convex optimization, in particular exponential cone programming, and dynamic programming techniques to bound and minimize the risk of an undetected outbreak by allocating budgeted resources each time step. We demonstrate with dynamic misinformation and epidemic examples how the method can provide targeted allocation of resources.