Link prediction via sparse Gaussian graphical model
type: Item
instance of: scholarly article
title: Link prediction via sparse Gaussian graphical model
zbMATH Open document ID: 1400.62120
DOI: 10.1155/2016/7213432
author: Liangliang Zhang
author: Longqi Yang
author: Guyu Hu
author: Zhisong Pan
author: Zhen Li
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 62H12
zbMATH DE Number: 6953516
Wikidata QID: Q59141153
describes a project that uses: glasso
describes a project that uses: QUIC
describes a project that uses: SLEP
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/7213432
OpenAlex ID: W2277731373
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description: scientific article; zbMATH DE number 6953516