纵向网络与节点属性的乘性协进化回归模型
Multiplicative Coevolution Regression Models for Longitudinal Networks and Nodal Attributes
摘要：
我们介绍了一个简单的和可扩展的协同进化模型的纵向网络和节点属性数据的分析。该模型的特征参数描述了三种现象：同质性，传染和自相关的网络和节点属性过程。同质性在这里描述了网络的变化如何与节点之间的节点属性相似性相关联。传染是指节点级属性如何根据网络而变化。我们提出的模型是基于一对相互交织的自回归过程。我们获得了连续值完全可观察网络和属性数据的最小二乘参数估计。我们还提供了其他几种情况下的贝叶斯推断方法，包括有序网络和属性数据，以及涉及潜在节点属性的模型。这些模型的扩展适用于国际关系数据的分析，并从青少年犯罪和友谊网络的研究数据。
英文摘要：
We introduce a simple and extendable coevolution model for the analysis of longitudinal network and nodal attribute data. The model features parameters that describe three phenomena: homophily, contagion and autocorrelation of the network and nodal attribute process. Homophily here describes how changes to the network may be associated with between-node similarities in terms of their nodal attributes. Contagion refers to how node-level attributes may change depending on the network. The model we present is based upon a pair of intertwined autoregressive processes. We obtain least-squares parameter estimates for continuous-valued fully-observed network and attribute data. We also provide methods for Bayesian inference in several other cases, including ordinal network and attribute data, and models involving latent nodal attributes. These model extensions are applied to an analysis of international relations data and to data from a study of teen delinquency and friendship networks.
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