Electrical Engineering and Computer Science, Syracuse University, NY, Syracuse, USA.
School of Information Studies, Syracuse University, NY, Syracuse, USA.
J R Soc Interface. 2023 Jan;20(198):20220555. doi: 10.1098/rsif.2022.0555. Epub 2023 Jan 4.
It has been observed that real-world social networks often exhibit stratification along economic or other lines, with consequences for class mobility and access to opportunities. With the rise in human interaction data and extensive use of online social networks, the structure of social networks (representing connections between individuals) can be used for measuring stratification. However, although stratification has been studied extensively in the social sciences, there is no single, generally applicable metric for measuring the level of stratification in a network. In this work, we first propose the novel Stratification Assortativity (StA) metric, which measures the extent to which a network is stratified into different tiers. Then, we use the StA metric to perform an in-depth analysis of the stratification of five co-authorship networks. We examine the evolution of these networks over 50 years and show that these fields demonstrate an increasing level of stratification over time, and, correspondingly, the trajectory of a researcher's career is increasingly correlated with her entry point into the network.
人们已经观察到,现实世界中的社交网络往往会沿着经济或其他方面呈现出分层现象,这对阶级流动和机会获取都有影响。随着人类互动数据的增加和在线社交网络的广泛使用,社交网络的结构(代表个体之间的联系)可用于衡量分层。然而,尽管分层现象在社会科学中已经得到了广泛的研究,但目前还没有一种单一的、普遍适用的指标来衡量网络中的分层程度。在这项工作中,我们首先提出了新的分层聚类系数(Stratification Assortativity,StA)指标,该指标衡量了网络分层为不同层次的程度。然后,我们使用 StA 指标对五个合著网络的分层进行了深入分析。我们研究了这些网络在 50 年中的演变,并表明这些领域随着时间的推移呈现出越来越高的分层程度,相应地,研究人员的职业轨迹与其进入网络的切入点越来越相关。