# Spread Control Method on Unknown Networks Based on Hierarchical Reinforcement Learning

arXiv: https://arxiv.org/abs/2308.14311 · HTML: https://mlsanity.com/paper/2308.14311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Spectral Methods for Immunization of Large Networks](https://mlsanity.com/paper/1711.00791/) (2017)
- [Optimal Vaccine Allocation to Control Epidemic Outbreaks in Arbitrary Networks](https://mlsanity.com/paper/1303.3984/) (2013)
- [Group based Centrality for Immunization of Complex Networks](https://mlsanity.com/paper/1812.11535/) (2018)

## Abstract (arXiv)

Epidemics such as COVID-19 pose serious threats to public health and our society, and it is critical to investigate effective methods to control the spread of epidemics over networks. Prior works on epidemic control often assume complete knowledge of network structures, a presumption seldom valid in real-world situations. In this paper, we study epidemic control on networks with unknown structures, and propose a hierarchical reinforcement learning framework for joint network structure exploration and epidemic control. To reduce the action space and achieve computation tractability, our proposed framework contains three modules: the Policy Selection Module, which determines whether to explore the structure or remove nodes to control the epidemic; the Explore Module, responsible for selecting nodes to explore; and the Remove Module, which decides which nodes to remove to stop the epidemic spread. Simulation results show that our proposed method outperforms baseline methods.