Representative community divisions of networks

TL;DR

This paper introduces a method to analyze multiple high-scoring community partitions in networks by grouping similar partitions and selecting archetypal representatives, providing a clearer understanding of community structures.

Contribution

The paper presents a novel approach to summarize large sets of high-scoring network partitions through clustering and archetypal partition identification.

Findings

Effective grouping of similar partitions

Clearer interpretation of community structures

Application to various example 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, 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.