School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China.
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China.
Sci Rep. 2016 Dec 15;6:38938. doi: 10.1038/srep38938.
Many link prediction methods have been developed to infer unobserved links or predict latent links based on the observed network structure. However, due to network noises and irregular links in real network, the performances of existed methods are usually limited. Considering random noises and irregular links, we propose a perturbation-based framework based on Non-negative Matrix Factorization to predict missing links. We first automatically determine the suitable number of latent features, which is inner rank in NMF, by Colibri method. Then, we perturb training set of a network by perturbation sets many times and get a series of perturbed networks. Finally, the common basis matrix and coefficients matrix of these perturbed networks are obtained via NMF and form similarity matrix of the network for link prediction. Experimental results on fifteen real networks show that the proposed framework has competitive performances compared with state-of-the-art link prediction methods. Correlations between the performances of different methods and the statistics of networks show that those methods with good precisions have similar consistence.
许多链接预测方法已经被开发出来，以基于观察到的网络结构推断未观察到的链接或预测潜在的链接。然而，由于网络噪声和真实网络中的不规则链接，现有方法的性能通常受到限制。考虑到随机噪声和不规则链接，我们提出了一种基于非负矩阵分解的基于摄动的框架，用于预测缺失的链接。我们首先通过 Colibri 方法自动确定合适的潜在特征数量，这是 NMF 中的内阶。然后，我们通过摄动集多次摄动网络的训练集，并获得一系列摄动网络。最后，通过 NMF 得到这些摄动网络的公共基矩阵和系数矩阵，并形成网络的相似性矩阵用于链接预测。在十五个真实网络上的实验结果表明，与最先进的链接预测方法相比，所提出的框架具有竞争力的性能。不同方法的性能与网络统计之间的相关性表明，那些具有良好精度的方法具有相似的一致性。
