Joint Modeling of Longitudinal Relational Data and Exogenous Variables
Joint Modeling of Longitudinal Relational Data and Exogenous Variables
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DOI：
10.1214/19-ba1160
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发表时间：
2020-06
影响因子：
4.4
通讯作者：
Rajarshi Guhaniyogi;Abel Rodríguez
中科院分区：
文献类型：
--
作者：
Rajarshi Guhaniyogi;Abel Rodríguez
本文提出了一种基于共享的、时变的随机潜在因子模型的框架，用于对网络和节点属性随时间共同演化的关系数据进行建模。我们提出的框架是灵活的，足以处理分类和连续属性，使我们能够估计潜在的社会空间的维度，并自动产生贝叶斯假设检验网络结构和节点属性之间的关联。此外，该模型易于计算，并容易产生推理和预测节点之间的缺失链接。我们采用我们的模型框架，研究22个国家和国家的具体指标在11年的时间内的国际关系的共同演变。
This article proposes a framework based on shared, time varying stochastic latent factor models for modeling relational data in which network and node-attributes co-evolve over time. Our proposed framework is flexible enough to handle both categorical and continuous attributes, allows us to estimate the dimension of the latent social space, and automatically yields Bayesian hypothesis tests for the association between network structure and nodal attributes. Additionally, the model is easy to compute and readily yields inference and prediction for missing link between nodes. We employ our model framework to study coevolution of international relations between 22 countries and the country specific indicators over a period of 11 years.