Inference of a universal social scale and segregation measures using social connectivity kernels
Inference of a universal social scale and segregation measures using social connectivity kernels
复制标题
DOI：
10.1098/rsif.2020.0638
复制
发表时间：
2020-10-28
影响因子：
3.9
通讯作者：
Jones, Nick S.
中科院分区：
文献类型：
--
作者：
Hoffmann, Till;Jones, Nick S.
人们如何相互联系是社会科学中的一个基本问题，由此产生的社交网络会对我们的日常生活产生深远的影响。布劳给出了一个强有力的解释：人们基于他们在社会空间中的位置而相互联系。然而，社会距离的原则性衡量标准，允许社会内部和社会之间的比较，仍然难以捉摸。我们使用条件独立边模型的连接内核来开发一系列具有理想属性的隔离统计：它们提供了一个直观和通用的社会空间特征尺度（便于跨数据集和社会的比较），适用于多变量和混合节点属性，并捕获隔离在个人，个人对和社会作为一个整体。我们证明了隔离统计可以在Blau空间（一个由社会成员的属性所跨越的空间）上导出一个度量，并提供两个社会的映射。在贝叶斯范式下，我们从英国和美国的四项调查中收集的11个自我网络数据集推断连接内核的参数。布劳空间不同维度的重要性在时间和地点上是相似的，这表明宏观上稳定的社会结构。身体分离和年龄差异对友谊网络中的隔离影响最大，并对晚年的代际混合和隔离产生影响。
How people connect with one another is a fundamental question in the social sciences, and the resulting social networks can have a profound impact on our daily lives. Blau offered a powerful explanation: people connect with one another based on their positions in a social space. Yet a principled measure of social distance, allowing comparison within and between societies, remains elusive. We use the connectivity kernel of conditionally independent edge models to develop a family of segregation statistics with desirable properties: they offer an intuitive and universal characteristic scale on social space (facilitating comparison across datasets and societies), are applicable to multivariate and mixed node attributes, and capture segregation at the level of individuals, pairs of individuals and society as a whole. We show that the segregation statistics can induce a metric on Blau space (a space spanned by the attributes of the members of society) and provide maps of two societies. Under a Bayesian paradigm, we infer the parameters of the connectivity kernel from 11 ego-network datasets collected in four surveys in the UK and USA. The importance of different dimensions of Blau space is similar across time and location, suggesting a macroscopically stable social fabric. Physical separation and age differences have the most significant impact on segregation within friendship networks with implications for intergenerational mixing and isolation in later stages of life.