Optimal Resource Allocation for Network Protection Against Spreading Processes
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Abstract (arXiv)
We study the problem of containing spreading processes in arbitrary directed networks by distributing protection resources throughout the nodes of the network. We consider two types of protection resources are available: (i) Preventive resources able to defend nodes against the spreading (such as vaccines in a viral infection process), and (ii) corrective resources able to neutralize the spreading after it has reached a node (such as antidotes). We assume that both preventive and corrective resources have an associated cost and study the problem of finding the cost-optimal distribution of resources throughout the nodes of the network. We analyze these questions in the context of viral spreading processes in directed networks. We study the following two problems: (i) Given a fixed budget, find the optimal allocation of preventive and corrective resources in the network to achieve the highest level of containment, and (ii) when a budget is not specified, find the minimum budget required to control the spreading process. We show that both resource allocation problems can be solved in polynomial time using Geometric Programming (GP) for arbitrary directed graphs of nonidentical nodes and a wide class of cost functions. Furthermore, our approach allows to optimize simultaneously over both preventive and corrective resources, even in the case of cost functions being node-dependent. We illustrate our approach by designing optimal protection strategies to contain an epidemic outbreak that propagates through an air transportation network.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2013.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 2.
4 papers.
- 2021A purely data-driven framework for prediction, optimization, and control of networked processes: application to networked SIS epidemic modelcited by 1
- 2022Controlling Epidemic Spread using Probabilistic Diffusion Models on Networkscited by 0
- 2026Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertaintycited by 0
- 2026Network World Models as Environments for Algorithm Design on Complex Systemscited by 0
- 2026Network World Models as Environments for Algorithm Design on Complex Systems1 Oct 2026
- 2026Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty25 May 2026
- 2022Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks16 Feb 2022
- 2021A purely data-driven framework for prediction, optimization, and control of networked processes: application to networked SIS epidemic model1 Aug 2021