In this article we focus on dynamic network data which describe interactions among a fixed population through time. We model this data using the latent space framework, in which the probability of a connection forming is expressed as a function of low-dimensional latent coordinates associated with the nodes, and consider sequential estimation of model parameters via Sequential Monte Carlo (SMC) methods. In this setting, SMC is a natural candidate for estimation which offers greater scalability than existing approaches commonly considered in the literature, allows for estimates to be conveniently updated given additional observations and facilitates both online and offline inference. We present a novel approach to sequentially infer parameters of dynamic latent space network models by building on techniques from the high-dimensional SMC literature. Furthermore, we examine the scalability and performance of our approach via simulation, demonstrate the flexibility of our approach to model variants and analyse a real-world dataset describing classroom contacts.
翻译:在本篇文章中,我们注重动态网络数据,以描述固定人口在时间上的相互作用。我们利用潜伏空间框架对这些数据进行模型模型,其中连接的概率表现为与节点相关的低维潜在坐标的函数,并考虑通过Squesttial Monte Carlo(SMC)方法对模型参数进行顺序估计。在这一背景下,SMC是进行估算的自然候选体,比文献中通常考虑的现有方法具有更大的可缩放性,这样可以方便地对估计数进行更新,同时进行更多的观测,并便利在线和离线推理。我们提出了一个新颖的方法,利用高维SMC文献的技术,按顺序推导动态潜伏空间网络模型的参数。此外,我们通过模拟来审查我们的方法的可缩放性和性,展示我们模型变量的方法的灵活性,并分析描述课堂接触的真实世界数据集。