  Many real-world complex networks actually have a bipartite nature: their
nodes may be separated into two classes, the links being between nodes of
different classes only. Despite this, and despite the fact that many ad-hoc
tools have been designed for the study of special cases, very few exist to
analyse (describe, extract relevant information) such networks in a systematic
way. We propose here an extension of the most basic notions used nowadays to
analyse classical complex networks to the bipartite case. To achieve this, we
introduce a set of simple statistics, which we discuss by comparing their
values on a representative set of real-world networks and on their random
versions.
