Conventional social network analysis typically focuses on analyzing the structure of the connections between pairs of nodes in a sample dataset. However, the process and the consequences of how long it takes pairs of nodes to be connected, i.e., node connection times, on the network structure have been understudied in the literature. In this article, we propose a novel statistical approach, so-called the Bayesian latent space accumulator model, for modeling connection times and their influence on the structure of connections. We focus on a special type of bipartite network composed of respondents and test items, where connection outcomes are binary and mutually exclusive. To model connection times for each connection outcome, we leverage ideas from the competing risk modeling approach and embed latent spaces into the competing risk models to capture heterogeneous dependence structures of connection times across connection outcome types. The proposed approach is applied and illustrated with two real data examples.
翻译:常规社会网络分析通常侧重于分析抽样数据集中对结点之间连接的结构,然而,文献中未充分研究对网络结构进行网络连接需要多长时间的结点,即节点连接时间的过程和后果。在本篇文章中,我们提出一种新的统计方法,即所谓的巴耶斯潜伏空间积累模型,用于模拟连接时间及其对连接结构的影响。我们侧重于一种特殊类型的双方网络,由回答者和测试项目组成,其中连接结果是二元和相互排斥的。为模拟每个连接结果的连接时间,我们利用相互竞争的风险模型方法中的想法,并将潜在空间嵌入相互竞争的风险模型中,以捕捉连接结果类型之间连接时间的多种依赖性结构。拟议方法应用并用两个真实的数据实例加以说明。