2023/06/11 by Boning Li, Gojko Čutura, Li, Boning +5
Physics and Astronomy · Psychology · Social Sciences · #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Mental Health Research Topics #Signal Processing (eess.SP) #Social and Information Networks (cs.SI) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2306.07938
openalex publication_date 2023/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose the deep demixing (DDmix) model, a graph autoencoder that can reconstruct epidemics evolving over networks from partial or aggregated temporal information. Assuming knowledge of the network topology but not of the epidemic model, our goal is to estimate the complete propagation path of a disease spread. A data-driven approach is leveraged to overcome the lack of model awareness. To solve this inverse problem, DDmix is proposed as a graph conditional variational autoencoder that is trained from past epidemic spreads. DDmix seeks to capture key aspects of the underlying (unknown) spreading dynamics in its latent space. Using epidemic spreads simulated in synthetic and real-world networks, we demonstrate the accuracy of DDmix by comparing it with multiple (non-graph-aware) learning algorithms. The generalizability of DDmix is highlighted across different types of networks. Finally, we showcase that a simple post-processing extension of our proposed method can help identify super-spreaders in the reconstructed propagation path.