This paper endeavors to learn time-varying graphs by using structured temporal priors that assume underlying relations between arbitrary two graphs in the graph sequence. Different from many existing chain structure based methods in which the priors like temporal homogeneity can only describe the variations of two consecutive graphs, we propose a structure named \emph{temporal graph} to characterize the underlying real temporal relations. Under this framework, the chain structure is actually a special case of our temporal graph. We further proposed Alternating Direction Method of Multipliers (ADMM), a distributed algorithm, to solve the induced optimization problem. Numerical experiments demonstrate the superiorities of our method.
翻译:本文试图通过使用结构化的时间顺序来学习时间变化图, 以假设图形序列中任意两个图形之间的内在关系。 与许多基于链结构的现有方法不同, 前者如时间同质性只能描述两个连续图表的变异, 我们建议了一个名为 emph{ 时钟图} 的结构来描述潜在的实际时间关系。 在此框架下, 链结构实际上是我们时间图的一个特例 。 我们进一步建议了多种图形的交替方向法( ADMM ), 一种分布式算法, 来解决引致的优化问题。 数字实验显示了我们方法的优越性 。