# Optimal resource allocation for competitive spreading processes on bilayer networks

arXiv: https://arxiv.org/abs/1512.05299 · HTML: https://mlsanity.com/paper/1512.05299/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2015.

## Built on by (most cited first, 6 of 6)

- [On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)](https://mlsanity.com/paper/2109.04024/) (2021)
- [Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State](https://mlsanity.com/paper/2301.06889/) (2023)
- [Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?](https://mlsanity.com/paper/2203.00035/) (2022)
- [Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)](https://mlsanity.com/paper/2209.07437/) (2022)
- [On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03491/) (2022)
- [A purely data-driven framework for prediction, optimization, and control of networked processes: application to networked SIS epidemic model](https://mlsanity.com/paper/2108.02005/) (2021)

## Abstract (arXiv)

This paper studies the SI1SI2S spreading model of two competing behaviors over a bilayer network. We address the problem of determining resource allocation strategies which design a spreading network so as to ensure the extinction of a selected process. Our discussion begins by extending the SI1SI2S model to edge-dependent infection and node-dependent recovery parameters with generalized graph topologies, which builds upon prior work that studies the homogeneous case. We then find conditions under which the mean-field approximation of a chosen epidemic process stabilizes to extinction exponentially quickly. Leveraging this result, we formulate and solve an optimal resource allocation problem in which we minimize the expenditure necessary to force a chosen epidemic process to become extinct as quickly as possible. In the case that the budget is not sufficient to ensure extinction of the desired process, we instead minimize a useful heuristic. We explore the efficacy of our methods by comparing simulations of the stochastic process to the mean-field model, and find that the mean-field methods developed work well for the optimal cost networks designed, but suffer from inaccuracy in other situations.