# Deep Demixing: Reconstructing the Evolution of Epidemics Using Graph Neural Networks

arXiv: https://arxiv.org/abs/2011.09583 · HTML: https://mlsanity.com/paper/2011.09583/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Estimating the State of Epidemics Spreading with Graph Neural Networks](https://mlsanity.com/paper/2105.05060/) (2021)
- [A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters](https://mlsanity.com/paper/2110.00844/) (2021)
- [GraSSRep: Graph-Based Self-Supervised Learning for Repeat Detection in Metagenomic Assembly](https://mlsanity.com/paper/2402.09381/) (2024)
- [Deep Demixing: Reconstructing the Evolution of Network Epidemics](https://mlsanity.com/paper/2306.07938/) (2023)

## Abstract (arXiv)

We study the temporal reconstruction of epidemics evolving over networks. Given partial or aggregated temporal information of the epidemic, our goal is to estimate the complete evolution of the spread leveraging the topology of the network but being agnostic to the precise epidemic model. We overcome this lack of model awareness through a data-driven solution to the inverse problem at hand. In particular, we propose DDmix, a graph conditional variational autoencoder that can be trained from past epidemic spreads and whose latent space seeks to capture key aspects of the underlying (unknown) spreading dynamics. We illustrate the accuracy and generalizability of DDmix and compare it with non-graph-aware learning algorithms through numerical experiments on epidemic spreads simulated on synthetic and real-world networks.