Link prediction in dynamic networks using random dot product graphs
Link prediction in dynamic networks using random dot product graphs
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DOI：
10.1007/s10618-021-00784-2
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发表时间：
2019-12
影响因子：
4.8
通讯作者：
Francesco Sanna Passino;A. Bertiger;Joshua Neil;N. Heard
中科院分区：
文献类型：
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作者：
Francesco Sanna Passino;A. Bertiger;Joshua Neil;N. Heard
大型网络中的链路预测问题在社会科学、生物学和计算机安全等各种实际应用中都是一个重要的任务。本文详细介绍和分析了基于流行的随机点乘积图模型的链接预测的统计技术，并将其推广到动态设置。从网络安全的一个实际应用出发，证明了随机点积图不仅是推断多个网络之间差异的有力工具，而且对于预测目的和理解网络的时间演化也是有效的。链路的概率通过两个阶段的信息融合得到：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获时间动态。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，利用时间信息进行扩展。本文提出的方法被应用于许多模拟和真实世界的图形，显示出令人满意的结果。
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.