A latent space model for multilayer network data
A latent space model for multilayer network data
复制标题
多层网络数据的潜在空间模型
DOI：
10.1016/j.csda.2022.107432
复制
发表时间：
2022
影响因子：
1.8
通讯作者：
Betancourt, Brenda
中科院分区：
文献类型：
--
作者：
Sosa, Juan;Betancourt, Brenda
提出了一个贝叶斯统计模型，同时表征两个或多个社交网络定义在一个共同的演员集。该模型的关键特征是分层先验分布，允许用户联合表示整个系统，实现依赖和独立网络之间的折衷。除其他外，这样的规范提供了一种简单的方法，可以在低维欧几里得空间中可视化多层网络数据，生成反映参与者之间共识亲和力的加权网络，建立网络之间相关性的度量，评估主体形成的认知判断关于参与者之间的关系，并在不同的社交实例中执行集群任务。该模型的功能说明使用真实世界和合成数据集，考虑到不同类型的演员，大小和关系。
A Bayesian statistical model to simultaneously characterize two or more social networks defined over a common set of actors is proposed. The key feature of the model is a hierarchical prior distribution that allows the user to represent the entire system jointly, achieving a compromise between dependent and independent networks. Among others things, such a specification provides an easy way to visualize multilayer network data in a low-dimensional Euclidean space, generate a weighted network that reflects the consensus affinity between actors, establish a measure of correlation between networks, assess cognitive judgments that subjects form about the relationships among actors, and perform clustering tasks at different social instances. The model's capabilities are illustrated using real-world and synthetic datasets, taking into account different types of actors, sizes, and relations.