  Recent advances in the field of network embedding have shown that
low-dimensional network representation is playing a critical role in network
analysis. Most existing network embedding methods encode the local proximity of
a node, such as the first- and second-order proximities. While being efficient,
these methods are short of leveraging the global structural information between
nodes distant from each other. In addition, most existing methods learn
embeddings on one single fixed network, and thus cannot be generalized to
unseen nodes or networks without retraining. In this paper we present SPINE, a
method that can jointly capture the local proximity and proximities at any
distance, while being inductive to efficiently deal with unseen nodes or
networks. Extensive experimental results on benchmark datasets demonstrate the
superiority of the proposed framework over the state of the art.
