1 research outputs found
Ubiquitousness of link-density and link-pattern communities in real-world networks
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