Nowadays, link prediction is of vital importance in the operation of social network platforms. One typical application is to make accurate recommendation to enhance users’ activeness. In this article, we propose a latent space logistic regression model for link prediction. The model takes both the users’ attributes and the latent social space into consideration. Two pseudo maximum likelihood estimators are proposed for parameter estimation. They correspond to the concepts of reciprocity and transitivity, respectively, and are computationally efficient for large-scale social networks. Extensive simulation studies are provided to evaluate the finite sample performance of the newly proposed methodology. At last, a real data set of Sina Weibo is presented for illustration purposes.

中文翻译：

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通过潜在空间逻辑回归模型进行链接预测

如今，链接预测在社交网络平台的运营中至关重要。一种典型的应用是进行精准推荐以提高用户的活跃度。在本文中，我们提出了一种用于链接预测的潜在空间逻辑回归模型。该模型同时考虑了用户的属性和潜在的社交空间。提出了两个伪最大似然估计器用于参数估计。它们分别对应于互惠性和传递性的概念，并且对于大规模社交网络具有计算效率。提供了广泛的模拟研究来评估新提出的方法的有限样本性能。最后以新浪微博的真实数据集为例进行说明。