Title:Representative community divisions of networks

Abstract: 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. However, in real-world applications there are typically many competitive partitions with objective scores close to that of the global optimum and the true community structure is more properly represented by an entire set of high-scoring partitions than by just the single optimum. 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 solution to this problem in the form of an efficient method that clusters similar partitions into groups and then identifies an archetypal partition as a representative of each group. The result is a succinct, human-readable summary of the form and variety of community structure in any network. We demonstrate the method on a range of example networks.