  Network representations of systems from various scientific and societal
domains are neither completely random nor fully regular, but instead appear to
contain recurring structural building blocks. These features tend to be shared
by networks belonging to the same broad class, such as the class of social
networks or the class of biological networks. At a finer scale of
classification within each such class, networks describing more similar systems
tend to have more similar features. This occurs presumably because networks
representing similar purposes or constructions would be expected to be
generated by a shared set of domain specific mechanisms, and it should
therefore be possible to classify these networks into categories based on their
features at various structural levels. Here we describe and demonstrate a new,
hybrid approach that combines manual selection of features of potential
interest with existing automated classification methods. In particular,
selecting well-known and well-studied features that have been used throughout
social network analysis and network science and then classifying with methods
such as random forests that are of special utility in the presence of feature
collinearity, we find that we achieve higher accuracy, in shorter computation
time, with greater interpretability of the network classification results.
