  Link prediction is a paradigmatic problem in network science, which aims at
estimating the existence likelihoods of nonobserved links, based on known
topology. After a brief introduction of the standard problem and metrics of
link prediction, this Perspective will summarize representative progresses
about local similarity indices, link predictability, network embedding, matrix
completion, ensemble learning and others, mainly extracted from thousands of
related publications in the last decade. Finally, this Perspective will outline
some long-standing challenges for future studies.
