论文标题
数据驱动的图形模型链接预测
Data-driven Link Prediction over Graphical Models
论文作者
论文摘要
积极的链接预测（PLP）问题是在系统识别框架中提出的：我们考虑用于自动回归运动平均（ARMA）高斯随机过程的动态图形模型。为了识别参数，我们在两个不同的时间尺度上对网络进行建模：一个更快的时间尺度，我们假设代表代理动力学的过程可以被视为固定性，并且模型参数可能会变化的过程较慢。后者说明了新边缘的可能出现。识别问题被投入到优化框架中，可以将其视为识别ARMA图形模型的现有方法的概括。我们证明了这种优化问题的解决方案的存在和唯一性，并提出了一个程序来计算该解决方案。提供了测试我们方法的性能的模拟。
The positive link prediction (PLP) problem is formulated in a system identification framework: we consider dynamic graphical models for auto-regressive moving-average (ARMA) Gaussian random processes. For the identification of the parameters, we model our network on two different time scales: a quicker one, over which we assume that the process representing the dynamics of the agents can be considered to be stationary, and a slower one in which the model parameters may vary. The latter accounts for the possible appearance of new edges. The identification problem is cast into an optimization framework which can be seen as a generalization of the existing methods for the identification of ARMA graphical models. We prove the existence and uniqueness of the solution of such an optimization problem and we propose a procedure to compute numerically this solution. Simulations testing the performances of our method are provided.
