  Complex networks datasets often come with the problem of missing information:
interactions data that have not been measured or discovered, may be affected by
errors, or are simply hidden because of privacy issues. This Element provides
an overview of the ideas, methods and techniques to deal with this problem and
that together define the field of network reconstruction. Given the extent of
the subject, we shall focus on the inference methods rooted in statistical
physics and information theory. The discussion will be organized according to
the different scales of the reconstruction task, that is, whether the goal is
to reconstruct the macroscopic structure of the network, to infer its mesoscale
properties, or to predict the individual microscopic connections.
