Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 453546, 8 pages
Research Article

Link Prediction in Complex Network via Penalizing Noncontribution Relations of Endpoints

1State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
2School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China

Received 21 July 2014; Revised 10 September 2014; Accepted 24 September 2014; Published 16 November 2014

Academic Editor: Bo Shen

Copyright © 2014 Xuzhen Zhu 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.


Similarity based link prediction algorithms become the focus in complex network research. Although endpoint degree as source of influence diffusion plays an important role in link prediction, some noncontribution links, also called noncontribution relations, involved in the endpoint degree serve nothing to the similarity between the two nonadjacent endpoints. In this paper, we propose a novel link prediction algorithm to penalize those endpoints’ degrees including many null links in influence diffusion, namely, noncontribution relations penalization algorithm, briefly called NRP. Seven mainstream baselines are introduced for comparison on nine benchmark datasets, and numerical analysis shows great improvement of accuracy performance, measured by the Area Under roc Curve (AUC). At last, we simply discuss the complexity of our algorithm.