Cremers Jolien, Kohler Benjamin, Maier Benjamin Frank, Eriksen Stine Nymann, Einsiedler Johanna, Christensen Frederik Kølby, Lehmann Sune, Lassen David Dreyer, Mortensen Laust Hvas, Bjerre-Nielsen Andreas
Methods and Analysis, Statistics Denmark, 2100, Copenhagen, Denmark.
Center for Social Data Science (SODAS), University of Copenhagen, 1353, Copenhagen, Denmark.
Sci Rep. 2025 May 26;15(1):18383. doi: 10.1038/s41598-025-98072-2.
Social networks shape individuals' lives, influencing everything from career paths to health. This paper presents a registry-based, multi-layer and temporal network of the entire Danish population from 2008 to 2021. Our network maps the relationships formed through family, households, neighborhoods, colleagues and classmates for approximately 7.2 million individuals with more than 1.4 billion relations between them over the course of a decade. We outline key properties of this multiplex network, introducing both an individual-focused perspective as well as a bipartite representation. We show how to aggregate and combine the layers, and how to efficiently compute network measures such as shortest paths in large administrative networks. Our analysis reveals how past connections reappear later in other layers, that the number of relationships aggregated over time reflects the position in the income distribution, and that we can recover canonical shortest-path-length distributions when appropriately weighting connections. Along with the network data, we release a Python package that uses the bipartite network representation for efficient analysis.
社交网络塑造着个人生活，影响着从职业道路到健康等方方面面。本文展示了一个基于登记册的、多层且具有时间维度的2008年至2021年丹麦全体人口网络。我们的网络描绘了通过家庭、住户、邻里、同事和同学形成的关系，涉及约720万人，在十年间他们之间的关系超过14亿。我们概述了这个多重网络的关键属性，引入了以个人为中心的视角以及二分表示法。我们展示了如何聚合和合并各层，以及如何在大型行政网络中高效计算诸如最短路径等网络指标。我们的分析揭示了过去的联系如何在之后出现在其他层中，随着时间聚合的关系数量反映了在收入分配中的位置，并且当对联系进行适当加权时我们可以恢复标准的最短路径长度分布。连同网络数据，我们发布了一个Python包，它使用二分网络表示法进行高效分析。
