Link weight prediction is an important subject in network science and machine learning. Its applications to social network analysis, network modeling, and bioinformatics are ubiquitous. Although this subject has attracted considerable attention recently, the performance and interpretability of existing prediction models have not been well balanced. This article focuses on an unsupervised mixed strategy for link weight prediction. Here, the target attribute is the link weight, which represents the correlation or strength of the interaction between a pair of nodes. The input of the model is the weighted adjacency matrix without any preprocessing, as widely adopted in the existing models. Extensive observations on a large number of networks show that the new scheme is competitive to the state-of-the-art algorithms concerning both root-mean-square error and Pearson correlation coefficient metrics. Analytic and simulation results suggest that combining the weight consistency of the network and the link weight-associated latent factors of the nodes is a very effective way to solve the link weight prediction problem.

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使用权重扰动和潜在因子的链接权重预测

链路权重预测是网络科学和机器学习中的一个重要课题。它在社交网络分析、网络建模和生物信息学中的应用无处不在。尽管该主题最近引起了相当大的关注，但现有预测模型的性能和可解释性并没有得到很好的平衡。本文重点介绍一种用于链接权重预测的无监督混合策略。这里，目标属性是链接权重，它表示一对节点之间交互的相关性或强度。模型的输入是加权邻接矩阵，没有任何预处理，在现有模型中被广泛采用。对大量网络的广泛观察表明，新方案在均方根误差和皮尔逊相关系数指标方面与最先进的算法具有竞争力。分析和仿真结果表明，将网络的权重一致性与节点的链接权重相关潜在因素相结合是解决链接权重预测问题的一种非常有效的方法。