# Bayesian inference of network structure from information cascades

arXiv: https://arxiv.org/abs/1908.03318 · HTML: https://mlsanity.com/paper/1908.03318/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2019.

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

- [Learning Latent Graph Structures and their Uncertainty](https://mlsanity.com/paper/2405.19933/) (2024)
- [Graph Structure Learning with Interpretable Bayesian Neural Networks](https://mlsanity.com/paper/2406.14786/) (2024)
- [A Bayesian Approach for the Network Reconstruction of Interdependent Critical Infrastructure Systems from Cascading Failures](https://mlsanity.com/paper/2211.15590/) (2022)
- [DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property](https://mlsanity.com/paper/2310.01696/) (2023)
- [Reconstructing networks from simple and complex contagions](https://mlsanity.com/paper/2405.00129/) (2024)
- [Learn to Vaccinate: Combining Structure Learning and Effective Vaccination for Epidemic and Outbreak Control](https://mlsanity.com/paper/2506.15397/) (2025)

## Abstract (arXiv)

Contagion processes are strongly linked to the network structures on which they propagate, and learning these structures is essential for understanding and intervention on complex network processes such as epidemics and (mis)information propagation. However, using contagion data to infer network structure is a challenging inverse problem. In particular, it is imperative to have appropriate measures of uncertainty in network structure estimates, however these are largely ignored in most machine-learning approaches. We present a probabilistic framework that uses samples from the distribution of networks that are compatible with the dynamics observed to produce network and uncertainty estimates. We demonstrate the method using the well known independent cascade model to sample from the distribution of networks P(G) conditioned on the observation of a set of infections C. We evaluate the accuracy of the method by using the marginal probabilities of each edge in the distribution, and show the bene ts of quantifying uncertainty to improve estimates and understanding, particularly with small amounts of data.