# Reachability analysis in stochastic directed graphs by reinforcement learning

arXiv: https://arxiv.org/abs/2202.12546 · HTML: https://mlsanity.com/paper/2202.12546/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 1 of 1)

- [Gossips and Prejudices: Ergodic Randomized Dynamics in Social Networks](https://mlsanity.com/paper/1304.2268/) (2013)

## Abstract (arXiv)

We characterize the reachability probabilities in stochastic directed graphs by means of reinforcement learning methods. In particular, we show that the dynamics of the transition probabilities in a stochastic digraph can be modeled via a difference inclusion, which, in turn, can be interpreted as a Markov decision process. Using the latter framework, we offer a methodology to design reward functions to provide upper and lower bounds on the reachability probabilities of a set of nodes for stochastic digraphs. The effectiveness of the proposed technique is demonstrated by application to the diffusion of epidemic diseases over time-varying contact networks generated by the proximity patterns of mobile agents.