The Exponential-family Random Graph Model (ERGM) is a powerful model to fit networks with complex structures. However, for dynamic valued networks whose observations are matrices of counts that evolve over time, the development of the ERGM framework is still in its infancy. To facilitate the modeling of dyad value increment and decrement, a Partially Separable Temporal ERGM is proposed for dynamic valued networks. The parameter learning algorithms inherit state-of-the-art estimation techniques to approximate the maximum likelihood, by drawing Markov chain Monte Carlo (MCMC) samples conditioning on the valued network from the previous time step. The ability of the proposed model to interpret network dynamics and forecast temporal trends is demonstrated with real data.

翻译：指数族随机图模型（ERGM）是拟合具有复杂结构网络的有力工具。然而，对于观测数据为随时间演化的计数矩阵的动态加权网络，ERGM框架的发展仍处于初期阶段。为促进对双边关系值增减过程的建模，本文提出了一种面向动态加权网络的部分可分离时间ERGM。参数学习算法通过继承最先进的估计技术，基于前一时刻加权网络进行条件采样（采用马尔可夫链蒙特卡洛方法，即MCMC），从而近似极大似然估计。通过真实数据验证了该模型解释网络动力学与预测时间趋势的能力。