# Exposure theory for learning complex networks with random walks

arXiv: https://arxiv.org/abs/2202.11262 · HTML: https://mlsanity.com/paper/2202.11262/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2022.

## Built on by (most cited first, 2 of 2)

- [Quantifying Human Priors over Social and Navigation Networks](https://mlsanity.com/paper/2402.18651/) (2024)
- [Origins and mitigation of double descent in reduced order modeling](https://mlsanity.com/paper/2607.26414/) (2026)

## Abstract (arXiv)

Random walks are a common model for exploration and discovery of complex networks. While numerous algorithms have been proposed to map out an unknown network, a complementary question arises: in a known network, which nodes and edges are most likely to be discovered by a random walker in finite time? Here we introduce exposure theory, a statistical mechanics framework that predicts the learning of nodes and edges across several types of networks, including weighted and temporal, and show that edge learning follows a universal trajectory. While the learning of individual nodes and edges is noisy, exposure theory produces a highly accurate prediction of aggregate exploration statistics.