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Mathematical Problems in Engineering
Volume 2016, Article ID 7213432, 11 pages
http://dx.doi.org/10.1155/2016/7213432
Research Article

Link Prediction via Sparse Gaussian Graphical Model

College of Command Information System, PLA University of Science and Technology, Nanjing 210007, China

Received 10 November 2015; Revised 27 January 2016; Accepted 27 January 2016

Academic Editor: David Bigaud

Copyright © 2016 Liangliang Zhang 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 is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC) value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.