  Link prediction aims to uncover the underlying relationship behind networks,
which could be utilized to predict the missing edges or identify the spurious
edges, and attracts much attention from various fields. The key issue of link
prediction is to estimate the likelihood of two nodes in networks. Most current
approaches of link prediction base on static structural analysis and ignore the
temporal aspects of evolving networks. Unlike previous work, in this paper, we
propose a popularity based structural perturbation method (PBSPM) that
characterizes the similarity of an edge not only from existing connections of
networks, but also from the popularity of its two endpoints, since popular
nodes have much more probability to form links between themselves. By taking
popularity of nodes into account, PBSPM could suppress nodes that have high
importance, but gradually become inactive. Therefore the proposed method is
inclined to predict potential edges between active nodes, rather than edges
between inactive nodes. Experimental results on four real networks show that
the proposed method outperforms the state-of-the-art methods both in accuracy
and robustness in evolving networks.
