Statistical significance of communities in networks
Lancichinetti, A, Radicchi, F, Ramasco, JJPhys. Rev. E 81, 046110 (2010)
Times cited: 43
Abstract
Nodes in real-world networks are usually organized in local modules.
These groups, called communities, are intuitively defined as subgraphs
with a larger density of internal connections than of external links.
In this work, we define a measure aimed at quantifying the statistical
significance of single communities. Extreme and order statistics are
used to predict the statistics associated with individual clusters in
random graphs. These distributions allows us to define one community
significance as the probability that a generic clustering algorithm
finds such a group in a random graph. The method is successfully
applied in the case of real-world networks for the evaluation of the
significance of their communities.