We study the patient zero problem in epidemic spreading processes in the independent cascade model and propose a geometric approach for source reconstruction. Using Johnson-Lindenstrauss projections, we embed the contact network into a low-dimensional Euclidean space and estimate the infection source as the node closest to the center of gravity of infected nodes. Simulations on Erd\H{o}s-R\'enyi graphs demonstrate that our estimator achieves meaningful reconstruction accuracy despite operating on compressed observations.
Cite
@article{arxiv.2604.16074,
title = {Finding Patient Zero via Low-Dimensional Geometric Embeddings},
author = {Stefan Huber and Dominik Kaaser},
journal= {arXiv preprint arXiv:2604.16074},
year = {2026}
}