  Network science investigates methodologies that summarise relational data to
obtain better interpretability. Identifying modular structures is a fundamental
task, and assessment of the coarse-grain level is its crucial step. Here, we
propose principled, scalable, and widely applicable assessment criteria to
determine the number of clusters in modular networks based on the leave-one-out
cross-validation estimate of the edge prediction error.
