The observed functional behavior of a wide variety large-scale systems is often the result of a network of pairwise interactions between individual elements. However, in many cases these interactions are hidden from us, either because they are impossible to be measured directly, or because their measurement can be done only at significant experimental cost. In such situations, we are required to infer the network of interactions from the observed functional behavior.

In this talk, I will present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior, that at the same time infers the modular structure (or "communities") present in the network. I will show how the joint reconstruction with community detection has a synergistic effect, where the edge correlations used to inform the existence of communities are also inherently used to improve the accuracy of the reconstruction which, in turn, can better inform the uncovering of communities. I will illustrate the use of the method with observations arising from epidemic models and the Ising model, both on synthetic and empirical networks, as well as on data containing only functional information.