标题： 在动态网络中选择潜在随机块模型的组数 显示英文标题

标题： Choosing the number of groups in a latent stochastic block model for dynamic networks

摘要： 潜在随机块模型是灵活的统计模型，广泛用于社会网络分析。 近年来，已经做出了努力将这些模型扩展到时间动态网络，其中节点之间的连接在多个不同时间点被观察到。 在本文中，我们通过使用马尔可夫性质来描述节点聚类成员随时间演变的情况，从而扩展了原始随机块模型。 我们将网络节点的聚类问题重新置于基于模型的框架中，并表明对于一些似然模型，集成完整似然可以进行解析计算。 然后，我们提出了一种可扩展的贪心算法来最大化这个量，从而在一个推断框架中估计最优划分和理想组数。 最后，我们提出了将我们的方法应用于真实和人工数据集的实例。 显示英文摘要

摘要： Latent stochastic block models are flexible statistical models that are widely used in social network analysis. In recent years, efforts have been made to extend these models to temporal dynamic networks, whereby the connections between nodes are observed at a number of different times. In this paper we extend the original stochastic block model by using a Markovian property to describe the evolution of nodes' cluster memberships over time. We recast the problem of clustering the nodes of the network into a model-based context, and show that the integrated completed likelihood can be evaluated analytically for a number of likelihood models. Then, we propose a scalable greedy algorithm to maximise this quantity, thereby estimating both the optimal partition and the ideal number of groups in a single inferential framework. Finally we propose applications of our methodology to both real and artificial datasets.