  Many real-world networks, including nervous systems, exhibit meso-scale
structure. This means that their elements can be grouped into meaningful
sub-networks. In general, these sub-networks are unknown ahead of time and must
be "discovered" algorithmically using community detection methods. In this
article, we review evidence that nervous systems exhibit meso-scale structure
in the form of communities, clusters, and modules. We also provide a set of
guidelines to assist users in applying community detection methods to their own
network data. These guidelines focus on the method of modularity maximization
but, in many cases, are general and applicable to other techniques.
