Unveiling the Social Fabric: A Temporal, Nation-Scale Social Network and its Characteristics

TL;DR

This paper constructs and analyzes a comprehensive, multi-layer temporal social network of Denmark's entire population from 2008 to 2021, revealing insights into social structure, connection reappearances, and income-related relationship patterns.

Contribution

It introduces a large-scale, multi-layer, temporal social network dataset and methods for analyzing and aggregating layers, along with an efficient Python analysis package.

Findings

Connections reappear across layers over time

Relationship counts correlate with income distribution

Canonical shortest path distributions can be recovered

Abstract

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.