  This work addresses the problem of simulating Gaussian random fields that are
continuously indexed over a class of metric graphs, termed graphs with
Euclidean edges, being more general and flexible than linear networks. We
introduce three general algorithms that allow to reconstruct a wide spectrum of
random fields having a covariance function that depends on a specific metric,
called resistance metric, and proposed in recent literature. The algorithms are
applied to a synthetic case study consisting of a street network. They prove to
be fast and accurate in that they reproduce the target covariance function and
provide random fields whose finite-dimensional distributions are approximately
Gaussian.
