  Link prediction plays an important role in understanding intrinsic evolving
mechanisms of networks. With the belief that the likelihood of the existence of
a link between two nodes is strongly related with their similarity, many
methods have been proposed to calculate node similarity based on node
attributes and/or topological structures. Among a large variety of methods that
take into account paths connecting the target pair of nodes, most of which
neglect the heterogeneity of those paths. Our hypothesis is that a path
consisting of small-degree nodes provides a strong evidence of similarity
between two ends, accordingly, we propose a so-called sig- nificant path index
in this Letter to leverage intermediate nodes' degrees in similarity
calculation. Empirical experiments on twelve disparate real networks
demonstrate that the proposed index outperforms the mainstream link prediction
baselines.

Physics and Society;Social and Information Networks;Data Analysis, Statistics and Probability