Predicting missing links via significant paths

¹ State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications Beijing, 100876, PRC
² Web Sciences Center, University of Electronic Science and Technology of China - Chengdu, 610054, PRC
³ Institute of Computing Technology, Chinese Academy of Sciences - Beijing, 100190, PRC

^(a) mail@huangjunming.com

Received: 25 February 2014
Accepted: 31 March 2014

Abstract

Link prediction plays an important role in understanding the intrinsic evolving mechanisms of networks. With the belief that the likelihood of the existence of a link between two nodes is strongly related to 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 them 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 significant 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.