Deep Demixing: Reconstructing the Evolution of Network Epidemics
Boning Li Gojko Čutura Ananthram Swami Santiago Segarra
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.