# Finding Patient Zero: Learning Contagion Source with Graph Neural Networks

arXiv: https://arxiv.org/abs/2006.11913 · HTML: https://mlsanity.com/paper/2006.11913/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [A Review of Graph Neural Networks in Epidemic Modeling](https://mlsanity.com/paper/2403.19852/) (2024)
- [Estimating the State of Epidemics Spreading with Graph Neural Networks](https://mlsanity.com/paper/2105.05060/) (2021)
- [GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion](https://mlsanity.com/paper/2403.00014/) (2024)
- [A Bayesian generative neural network framework for epidemic inference problems](https://mlsanity.com/paper/2111.03383/) (2021)
- [GraphBridge: Towards Arbitrary Transfer Learning in GNNs](https://mlsanity.com/paper/2502.19252/) (2025)
- [Sequential Attention Source Identification Based on Feature Representation](https://mlsanity.com/paper/2306.15886/) (2023)
- [Schemes of Propagation Models and Source Estimators for Rumor Source Detection in Online Social Networks: A Short Survey of a Decade of Research](https://mlsanity.com/paper/2101.00753/) (2021)
- [Accelerating Sparse Graph Neural Networks with Tensor Core Optimization](https://mlsanity.com/paper/2412.12218/) (2024)
- [Learning hidden cascades via classification](https://mlsanity.com/paper/2505.11228/) (2025)
- [Inference of a Rumor's Source in the Independent Cascade Model](https://mlsanity.com/paper/2205.12125/) (2022)
- [Deep Demixing: Reconstructing the Evolution of Network Epidemics](https://mlsanity.com/paper/2306.07938/) (2023)
- [HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion](https://mlsanity.com/paper/2505.12894/) (2025)
- [SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs](https://mlsanity.com/paper/2505.12910/) (2025)
- [Conformal Prediction for Multi-Source Detection on a Network](https://mlsanity.com/paper/2511.08867/) (2025)
- [Graph Neural Networks for Source Detection: A Review and Benchmark Study](https://mlsanity.com/paper/2512.20657/) (2025)

## Abstract (arXiv)

Locating the source of an epidemic, or patient zero (P0), can provide critical insights into the infection's transmission course and allow efficient resource allocation. Existing methods use graph-theoretic centrality measures and expensive message-passing algorithms, requiring knowledge of the underlying dynamics and its parameters. In this paper, we revisit this problem using graph neural networks (GNNs) to learn P0. We establish a theoretical limit for the identification of P0 in a class of epidemic models. We evaluate our method against different epidemic models on both synthetic and a real-world contact network considering a disease with history and characteristics of COVID-19. % We observe that GNNs can identify P0 close to the theoretical bound on accuracy, without explicit input of dynamics or its parameters. In addition, GNN is over 100 times faster than classic methods for inference on arbitrary graph topologies. Our theoretical bound also shows that the epidemic is like a ticking clock, emphasizing the importance of early contact-tracing. We find a maximum time after which accurate recovery of the source becomes impossible, regardless of the algorithm used.