揭开社会结构的面纱:国家规模的时态社会网络及其特征

Jolien Cremers, Benjamin Kohler, Benjamin Frank Maier, Stine Nymann Eriksen, Johanna Einsiedler, Frederik Kølby Christensen, Sune Lehmann, David Dreyer Lassen, Laust Hvas Mortensen, Andreas Bjerre-Nielsen
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引用次数: 0

摘要

社交网络塑造着个人的生活,影响着从职业道路到健康的方方面面。本文介绍了 2008-2021 年丹麦全体人口(约 720 万人)的基于登记的多层时空网络。我们的网络映射了通过家庭、住户、邻里、同事和同学形成的关系。我们概述了这一多重网络的关键属性,同时引入了以个人为中心的视角和双方格表示法。我们展示了如何聚合和组合层,以及如何在大型行政网络中有效计算网络度量(如最短路径)。我们的分析揭示了过去的连接是如何在其他层中重现的,随着时间的推移而汇总的关系数量反映了收入分布中的位置,而且在对连接进行适当加权时,我们可以恢复典型的最短路径长度分布。在发布网络数据的同时,我们还发布了一个 Python 软件包,它使用双向网络表示法进行高效分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling the Social Fabric: A Temporal, Nation-Scale Social Network and its Characteristics
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 in the years 2008-2021 (roughly 7.2 mill. individuals). Our network maps the relationships formed through family, households, neighborhoods, colleagues and classmates. 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.
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