Sparse Resource Allocation for Spreading Processes on Temporal-Switching Networks

TL;DR

This paper introduces a convex optimization framework using exponential cone programming and dynamic programming to efficiently allocate resources over time for controlling spreading processes on dynamic networks.

Contribution

It presents a novel approach for sparse resource allocation on temporal networks, addressing the challenge of time-varying connectivity in spreading process control.

Findings

Effective resource allocation in wildfire, epidemic, and misinformation scenarios.

Bounded and minimized outbreak risk through convex optimization.

Targeted resource deployment improves control efficiency.

Abstract

Spreading processes, e.g. epidemics, wildfires and rumors, are often modeled on static networks. However, their underlying network structures, e.g. changing contacts in social networks, different weather forecasts for wildfires, are due to ever-changing circumstances inherently time-varying in nature. In this paper, we therefore, 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 misinformation, epidemic and wildfire examples how the method can provide targeted allocation of resources.