Optimal Deployment of Resources for Maximizing Impact in Spreading Processes

TL;DR

This paper introduces a scalable dynamic message-passing framework to optimize resource deployment for controlling spreading processes on networks, considering heterogeneity and finite intervention windows.

Contribution

It presents a novel analytical approach that integrates network topology and dynamics to improve resource allocation strategies for spreading control.

Findings

Effective in real-world network examples

Outperforms purely topological methods

Handles heterogeneous interactions and finite time windows

Abstract

The effective use of limited resources for controlling spreading processes on networks is of prime significance in diverse contexts, ranging from the identification of "influential spreaders" for maximizing information dissemination and targeted interventions in regulatory networks, to the development of mitigation policies for infectious diseases and financial contagion in economic systems. Solutions for these optimization tasks that are based purely on topological arguments are not fully satisfactory; in realistic settings the problem is often characterized by heterogeneous interactions and requires interventions over a finite time window via a restricted set of controllable nodes. The optimal distribution of available resources hence results from an interplay between network topology and spreading dynamics. We show how these problems can be addressed as particular instances of a universal analytical framework based on a scalable dynamic message-passing approach and demonstrate the efficacy of the method on a variety of real-world examples.