  Graphs representing real world systems may be studied from their underlying
community structure. A community in a network is an intuitive idea for which
there is no consensus on its objective mathematical definition. The most used
metric in order to detect communities is the modularity, though many
disadvantages of this parameter have already been noticed in the literature. In
this work, we present a new approach based on a different metric: the surprise.
Moreover, the biases of different community detection algorithms and benchmark
networks are thoroughly studied, identified and commented about.
