In this talk we show that network ensembles approximating real
networks to the desired level of coarse graining can be constructed by
statistical mechanics methods.
These networks include networks with communities and networks embedded
in a real space having  an  heterogeneous degree distribution.
A interesting quantity that can be calculated is the  entropy of these
network ensembles.
The entropy of network ensembles  is able to quantify the
level of order present in complex networks by statistical mechanics
methods and  has various  theoretical
implications. In particular we will show that scale-free networks have
much smaller entropy than random graphs. This provides some
fundamental argument  in favor of the idea scale-free networks are  a
result of an non-equilibrium process.The entropy of random network
ensemble can be also used for inference problems. In particular  we will
show how we can  construct an entropy
based indicator able to asses the relevance of  features on a network.