Unveiling the social fabric through a temporal, nation-scale social network and its characteristics
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 from 2008 to 2021. Our network maps the relationships formed through family, households, neigh...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-98072-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850243456168886272 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Jolien Cremers |
| collection | DOAJ |
| description | 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 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. |
| format | Article |
| id | doaj-art-cac7f81c0acd4e6cb95bb5430124f754 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cac7f81c0acd4e6cb95bb5430124f7542025-08-20T02:00:00ZengNature PortfolioScientific Reports2045-23222025-05-0115111510.1038/s41598-025-98072-2Unveiling the social fabric through a temporal, nation-scale social network and its characteristicsJolien Cremers0Benjamin Kohler1Benjamin Frank Maier2Stine Nymann Eriksen3Johanna Einsiedler4Frederik Kølby Christensen5Sune Lehmann6David Dreyer Lassen7Laust Hvas Mortensen8Andreas Bjerre-Nielsen9Methods and Analysis, Statistics DenmarkMethods and Analysis, Statistics DenmarkMethods and Analysis, Statistics DenmarkMethods and Analysis, Statistics DenmarkMethods and Analysis, Statistics DenmarkMethods and Analysis, Statistics DenmarkCenter for Social Data Science (SODAS), University of CopenhagenCenter for Social Data Science (SODAS), University of CopenhagenMethods and Analysis, Statistics DenmarkCenter for Social Data Science (SODAS), University of CopenhagenAbstract 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.https://doi.org/10.1038/s41598-025-98072-2 |
| spellingShingle | 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 Unveiling the social fabric through a temporal, nation-scale social network and its characteristics Scientific Reports |
| title | Unveiling the social fabric through a temporal, nation-scale social network and its characteristics |
| title_full | Unveiling the social fabric through a temporal, nation-scale social network and its characteristics |
| title_fullStr | Unveiling the social fabric through a temporal, nation-scale social network and its characteristics |
| title_full_unstemmed | Unveiling the social fabric through a temporal, nation-scale social network and its characteristics |
| title_short | Unveiling the social fabric through a temporal, nation-scale social network and its characteristics |
| title_sort | unveiling the social fabric through a temporal nation scale social network and its characteristics |
| url | https://doi.org/10.1038/s41598-025-98072-2 |
| work_keys_str_mv | AT joliencremers unveilingthesocialfabricthroughatemporalnationscalesocialnetworkanditscharacteristics AT benjaminkohler unveilingthesocialfabricthroughatemporalnationscalesocialnetworkanditscharacteristics AT benjaminfrankmaier unveilingthesocialfabricthroughatemporalnationscalesocialnetworkanditscharacteristics AT stinenymanneriksen unveilingthesocialfabricthroughatemporalnationscalesocialnetworkanditscharacteristics AT johannaeinsiedler unveilingthesocialfabricthroughatemporalnationscalesocialnetworkanditscharacteristics AT frederikkølbychristensen unveilingthesocialfabricthroughatemporalnationscalesocialnetworkanditscharacteristics AT sunelehmann unveilingthesocialfabricthroughatemporalnationscalesocialnetworkanditscharacteristics AT daviddreyerlassen unveilingthesocialfabricthroughatemporalnationscalesocialnetworkanditscharacteristics AT lausthvasmortensen unveilingthesocialfabricthroughatemporalnationscalesocialnetworkanditscharacteristics AT andreasbjerrenielsen unveilingthesocialfabricthroughatemporalnationscalesocialnetworkanditscharacteristics |