The role of direct links for link prediction in evolving networks

¹ School of Marine Science and Technology, Northwestern Polytechnical University - Xi'an 710072, PRC
² School of Mathematics and Statistics, The University of Western Australia - Crawley, WA, 6009, Australia
³ Mineral Resources, CSIRO - Kensington, WA, 6151, Australia
⁴ College of Information and Communication Engineering, Dalian Minzu University - Dalian, Liaoning 116600, PRC

^(a) keke.shang.1989@gmail.com
^(b) wsyan@nwpu.edu.cn

Received: 28 November 2016
Accepted: 16 February 2017

Abstract

Link prediction is the problem of predicting the existence and location of unknown links from uncertain structural information of a network. Most existing accuracy measures do not consider the role of time evolution within the network. Moreover, almost all existing methods use indirect links to infer and evaluate the validity of links. We introduce time as a parameter for link prediction accuracy measures, and we modify the structure of the link prediction algorithms to exploit information of the known direct links for link prediction. We find that the direct link algorithm performs better than the indirect link algorithm for a range of time varying networks. We show that the network structure plays a more important role than weights for links prediction. In addition, our analysis finds that the number of common neighbours also plays an important role for the so-called weak-ties phenomenon.