  This chapter provides a comprehensive and self-contained discussion of the
most recent developments of information theory of networks. Maximum entropy
models of networks are the least biased ensembles enforcing a set of
constraints and are used in a number of application to produce null model of
networks. Here maximum entropy ensembles of networks are introduced by
distinguishing between microcanonical and canonical ensembles revealing the the
non-equivalence of these two classes of ensembles in the case in which an
extensive number of constraints is imposed. It is very common that network data
includes also meta-data describing feature of the nodes such as their position
in a real or in an abstract space. The features of the nodes can be treated as
latent variables that determine the cost associated to each link. Maximum
entropy network ensembles with latent variables include spatial networks and
their generalisation. In this chapter we cover the case of transportation
networks including airport and rail networks. Maximum entropy network ensemble
satisfy a given set of constraints. However traditional approaches do not
provide any insight on the origin of such constraints. We use information
theory principles to find the optimal distribution of latent variables in the
framework of the classical information theory of networks. This theory explains
the "blessing of non-uniformity" of data guaranteeing the efficiency of
inference algorithms.
