University of Amsterdam, Amsterdam, The Netherlands.
Statistics Netherlands (CBS), The Hague, The Netherlands.
Sci Rep. 2023 Jun 6;13(1):9209. doi: 10.1038/s41598-023-36324-9.
Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level.
大规模的人类社交网络结构通常是从在线社交媒体平台或移动通信数据的数字痕迹样本中推断出来的。相反，在这里我们研究了一个完整人群的社交网络结构，其中人们通过家庭、家庭、工作、学校和隔壁邻居的行政登记处的高质量链接连接。我们通过网络分析中的三个常见概念来研究这个多层次的社交机会结构：度、闭合和距离。研究结果展示了特定的网络层如何有助于网络的普遍无标度和小世界特性。此外，我们提出了一种新的过剩闭合度量，并将其应用于生命历程的视角，以展示个体的社会机会结构如何随年龄、社会经济地位和教育水平而变化。
