Ubiquitousness of link-density and link-pattern communities in real-world networks

TL;DR

This paper introduces a propagation-based algorithm capable of detecting both link-density and link-pattern communities in various real-world networks, revealing the widespread presence of complex community structures.

Contribution

The novel algorithm simultaneously identifies link-density and link-pattern communities without prior knowledge, validated on synthetic and real-world networks.

Findings

Both community types are prevalent in real-world networks.

The algorithm effectively uncovers meaningful community structures.

Link-pattern communities are as ubiquitous as link-density ones.

Abstract

Community structure appears to be an intrinsic property of many complex real-world networks. However, recent work shows that real-world networks reveal even more sophisticated modules than classical cohesive (link-density) communities. In particular, networks can also be naturally partitioned according to similar patterns of connectedness among the nodes, revealing link-pattern communities. We here propose a propagation based algorithm that can extract both link-density and link-pattern communities, without any prior knowledge of the true structure. The algorithm was first validated on different classes of synthetic benchmark networks with community structure, and also on random networks. We have further applied the algorithm to different social, information, technological and biological networks, where it indeed reveals meaningful (composites of) link-density and link-pattern communities. The results thus seem to imply that, similarly as link-density counterparts, link-pattern communities appear ubiquitous in nature and design.