  It appeared recently that the classical random graph model used to represent
real-world complex networks does not capture their main properties. Since then,
various attempts have been made to provide accurate models. We study here a
model which achieves the following challenges: it produces graphs which have
the three main wanted properties (clustering, degree distribution, average
distance), it is based on some real-world observations, and it is sufficiently
simple to make it possible to prove its main properties. This model consists in
sampling a random bipartite graph with prescribed degree distribution. Indeed,
we show that any complex network may be viewed as a bipartite graph with some
specific characteristics, and that its main properties may be viewed as
consequences of this underlying structure. We also propose a growing model
based on this observation.
