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Complexity
Volume 2017 (2017), Article ID 8581365, 12 pages
https://doi.org/10.1155/2017/8581365
Research Article

Connecting Patterns Inspire Link Prediction in Complex Networks

Guangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Correspondence should be addressed to Hao Liao

Received 9 August 2017; Revised 27 November 2017; Accepted 6 December 2017; Published 27 December 2017

Academic Editor: Diego Garlaschelli

Copyright © 2017 Ming-Yang Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Link prediction uses observed data to predict future or potential relations in complex networks. An underlying hypothesis is that two nodes have a high likelihood of connecting together if they share many common characteristics. The key issue is to develop different similarity-evaluating approaches. However, in this paper, by characterizing the differences of the similarity scores of existing and nonexisting links, we find an interesting phenomenon that two nodes with some particular low similarity scores also have a high probability to connect together. Thus, we put forward a new framework that utilizes an optimal one-variable function to adjust the similarity scores of two nodes. Theoretical analysis suggests that more links of low similarity scores (long-range links) could be predicted correctly by our method without losing accuracy. Experiments in real networks reveal that our framework not only enhances the precision significantly but also predicts more long-range links than state-of-the-art methods, which deepens our understanding of the structure of complex networks.