The problem of predicting links in large networks is an important task in a variety of practical applications, including social sciences, biology and computer security. In this paper, statistical techniques for link prediction based on the popular random dot product graph model are carefully presented, analysed and extended to dynamic settings. Motivated by a practical application in cyber-security, this paper demonstrates that random dot product graphs not only represent a powerful tool for inferring differences between multiple networks, but are also efficient for prediction purposes and for understanding the temporal evolution of the network. The probabilities of links are obtained by fusing information at two stages: spectral methods provide estimates of latent positions for each node, and time series models are used to capture temporal dynamics. In this way, traditional link prediction methods, usually based on decompositions of the entire network adjacency matrix, are extended using temporal information. The methods presented in this article are applied to a number of simulated and real-world graphs, showing promising results.

预测大型网络中的链接问题是各种实际应用中的一项重要任务，包括社会科学、生物学和计算机安全。在本文中，基于流行的随机点积图模型的链接预测统计技术被仔细呈现、分析并扩展到动态设置。受网络安全实际应用的启发，本文证明随机点积图不仅代表了推断多个网络之间差异的强大工具，而且对于预测目的和理解网络的时间演变也很有效。链接的概率是通过在两个阶段融合信息获得的：谱方法提供每个节点的潜在位置估计，时间序列模型用于捕捉时间动态。这样，传统的链路预测方法，通常基于整个网络邻接矩阵的分解，使用时间信息进行扩展。本文中介绍的方法应用于许多模拟和现实世界的图，显示出有希望的结果。