  Methods for detecting community structure in networks typically aim to
identify a single best partition of network nodes into communities, often by
optimizing some objective function, but in real-world applications there may be
many competitive partitions with objective scores close to the global optimum
and one can obtain a more informative picture of the community structure by
examining a representative set of such high-scoring partitions than by looking
at just the single optimum. However, such a set can be difficult to interpret
since its size can easily run to hundreds or thousands of partitions. In this
paper we present a method for analyzing large partition sets by dividing them
into groups of similar partitions and then identifying an archetypal partition
as a representative of each group. The resulting set of archetypal partitions
provides a succinct, interpretable summary of the form and variety of community
structure in any network. We demonstrate the method on a range of example
networks.
