  Networks have provided extremely successful models of data and complex
systems. Yet, as combinatorial objects, networks do not have in general
intrinsic coordinates and do not typically lie in an ambient space. The process
of assigning an embedding space to a network has attracted lots of interest in
the past few decades, and has been efficiently applied to fundamental problems
in network inference, such as link prediction, node classification, and
community detection. In this review, we provide a user-friendly guide to the
network embedding literature and current trends in this field which will allow
the reader to navigate through the complex landscape of methods and approaches
emerging from the vibrant research activity on these subjects.
