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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, the authors focus on the inference methods rooted in
statistical physics and information theory. The discussion is
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.
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