论文标题
动态有价值网络的部分可分离模型
A Partially Separable Model for Dynamic Valued Networks
论文作者
论文摘要
指数家庭随机图模型（ERGM）是一个强大的模型，可拟合具有复杂结构的网络。但是，对于动态有价值的网络的观察是随着时间的流逝而发展的计数矩阵，ERGM框架的发展仍处于起步阶段。为了促进二元格值增量和减少的建模，为动态有价值的网络提出了部分可分离的时间ERGM。参数学习算法继承了最先进的估计技术，以近似最大似然，通过绘制马尔可夫链蒙特卡洛（MCMC）样品从上一个时间步骤开始在有价值的网络上调节。实际数据证明了所提出的模型解释网络动态和预测时间趋势的能力。
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.
