Nodes in real-world networks are usually organized in local modules. These
groups, called communities, are intuitively defined as sub-graphs with a larger
density of internal connections than of external links. In this work,
we introduce a new measure aimed to quantify 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.