  Link prediction aims to infer missing links or predicting the future ones
based on currently observed partial networks, it is a fundamental problem in
network science with tremendous real-world applications. However, conventional
link prediction approaches neither have high prediction accuracy nor being
capable of revealing the hidden information behind links. To address this
problem, we generalize the latest techniques in deep learning on graphs and
present a new link prediction model - DeepLinker. Instead of learning node
representation with the node label information, DeepLinker uses the links as
supervised information. Experiments on five graphs show that DeepLinker can not
only achieve the state-of-the-art link prediction accuracy, but also acquire
the efficient node representations and node centrality ranking as the
byproducts. Although the representations are obtained without any supervised
node label information, they still perform well on node ranking and node
classification tasks.
