A latent space model for cognitive social structures data
A latent space model for cognitive social structures data
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DOI：
10.1016/j.socnet.2020.12.002
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发表时间：
2021-05-01
期刊：
影响因子：
3.1
通讯作者：
Rodriguez, Abel
中科院分区：
文献类型：
--
作者：
Sosa, Juan;Rodriguez, Abel
本文介绍了一种新的方法来建模一组有向的，二进制网络的背景下，认知社会结构（CSS）的数据。我们采用相对主义的方法，其中没有假设的存在下真正的网络。更具体地说，我们依赖于一个广义线性模型，该模型采用双线性结构来模拟网络内的传递性效应，并对双线性效应进行分层规范，以跨网络借用信息。这是一个空间模型，其中每个个体对关系强度的感知可以通过行为者（自己和他人）在潜在社会空间上的感知位置来解释。该模式的一个关键目标是提供一种机制，正式评估每个行为者对自己社会角色的看法与群体其他成员的看法之间的一致性。我们的实验与真实的和模拟数据表明，我们的模型的能力是可比的，甚至上级，其他模型的CSS数据在文献中报道。
This paper introduces a novel approach for modeling a set of directed, binary networks in the context of cognitive social structures (CSSs) data. We adopt a relativist approach in which no assumption is made about the existence of an underlying true network. More specifically, we rely on a generalized linear model that incorporates a bilinear structure to model transitivity effects within networks, and a hierarchical specification on the bilinear effects to borrow information across networks. This is a spatial model, in which the perception of each individual about the strength of the relationships can be explained by the perceived position of the actors (themselves and others) on a latent social space. A key goal of the model is to provide a mechanism to formally assess the agreement between each actors' perception of their own social roles with that of the rest of the group. Our experiments with both real and simulated data show that the capabilities of our model are comparable with or, even superior to, other models for CSS data reported in the literature.