title: Statistical significance of communities in networks.
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.
authors.affiliation: Complex Networks Lagrange Laboratory, Turin, Italy and Physics Department, Politecnico di Torino, ISI Foundation, Turin, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Lancichinetti
authors.forename: Filippo
authors.initials: F
authors.lastname: Radicchi
authors.forename: José J
authors.initials: JJ
authors.lastname: Ramasco
mesh_terms: D000818:Animals; D019985:Benchmarking; D008962:Models, Theoretical; D011336:Probability