# Deep learning of contagion dynamics on complex networks

arXiv: https://arxiv.org/abs/2006.05410 · HTML: https://mlsanity.com/paper/2006.05410/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 16 of 16)

- [A Review of Graph Neural Networks in Epidemic Modeling](https://mlsanity.com/paper/2403.19852/) (2024)
- [A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification](https://mlsanity.com/paper/2410.22377/) (2024)
- [Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data](https://mlsanity.com/paper/2306.10275/) (2023)
- [Learning Interpretable Network Dynamics via Universal Neural Symbolic Regression](https://mlsanity.com/paper/2411.06833/) (2024)
- [Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes](https://mlsanity.com/paper/2310.16466/) (2023)
- [Neural Symbolic Regression of Complex Network Dynamics](https://mlsanity.com/paper/2410.11185/) (2024)
- [Stretched and measured neural predictions of complex network dynamics](https://mlsanity.com/paper/2301.04900/) (2023)
- [SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting](https://mlsanity.com/paper/2312.02646/) (2023)
- [Learning hidden cascades via classification](https://mlsanity.com/paper/2505.11228/) (2025)
- [Reconstructing networks from simple and complex contagions](https://mlsanity.com/paper/2405.00129/) (2024)
- [Physics-Inspired Spatial Temporal Graph Neural Networks for Predicting Industrial Chain Resilience](https://mlsanity.com/paper/2508.16836/) (2025)
- [Physics-Informed High-order Graph Dynamics Identification Learning for Predicting Complex Networks Long-term Dynamics](https://mlsanity.com/paper/2510.09082/) (2025)
- [Inferring Network Evolutionary History via Structure-State Coupled Learning](https://mlsanity.com/paper/2601.02121/) (2026)
- [Learning graph topology from metapopulation epidemic encoder-decoder](https://mlsanity.com/paper/2603.02349/) (2026)
- [Spatio-temporal stochastic graph-based learning for infectious disease forecasting](https://mlsanity.com/paper/2605.30662/) (2026)
- [Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach](https://mlsanity.com/paper/2606.07483/) (2026)

## Abstract (arXiv)

Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting the quantitative accuracy of their predictions and the complexity of the dynamics they can model. Here, we propose a complementary approach based on deep learning where the effective local mechanisms governing a dynamic on a network are learned from time series data. Our graph neural network architecture makes very few assumptions about the dynamics, and we demonstrate its accuracy using different contagion dynamics of increasing complexity. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Finally, we illustrate the applicability of our approach using real data of the COVID-19 outbreak in Spain. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.