Community detection allows one to decompose a network into its building blocks. While communities can be identified with a variety of methods, their relative importance cannot be easily derived.
In this Nature Communications paper, we introduce an algorithm to identify modules which are most promising for further analysis. Our method allows for more efficient evaluation of hypotheses brought forward by the analysis of complex networks and thus speeding-up scientific discovery process in experimental network sciences.
