Source detection on graphs

Spreading processes on networks (graphs) have become ubiquitous in modern society with prominent examples such as infections, rumors, excitations, contaminations, or disturbances. Finding the source of such processes based on observations is important and difficult. We abstract the problem mathematically as an optimization problem on graphs. For the deterministic setting we make connections to the metric dimension of a graph and introduce the concept of spread resolving sets. For the stochastic setting we propose a new algorithm combining parameter estimation and experimental design. We discuss well-posedness of the algorithm and show encouraging numerical results on a benchmark library.