The structured backbone of temporal social ties
Complex networks can be useful to describe social interactions but for large datasets one needs to identify significant links to extract information. While most existing methods work for static networks, here the authors propose a method to extract the backbone of significant links in temporal netwo...
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Nature Publishing Group
2019-01-01
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Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-018-08160-3 |
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doaj-b191de5147054cd1b835ec2a4cf012372021-05-11T12:06:14ZengNature Publishing GroupNature Communications2041-17232019-01-0110111110.1038/s41467-018-08160-3The structured backbone of temporal social tiesTeruyoshi Kobayashi0Taro Takaguchi1Alain Barrat2Graduate School of Economics, Center for Computational Social Science, Kobe UniversityTodaAix Marseille Univ, Université de Toulon, CNRS, CPTComplex networks can be useful to describe social interactions but for large datasets one needs to identify significant links to extract information. While most existing methods work for static networks, here the authors propose a method to extract the backbone of significant links in temporal networks.https://doi.org/10.1038/s41467-018-08160-3 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Teruyoshi Kobayashi Taro Takaguchi Alain Barrat |
spellingShingle |
Teruyoshi Kobayashi Taro Takaguchi Alain Barrat The structured backbone of temporal social ties Nature Communications |
author_facet |
Teruyoshi Kobayashi Taro Takaguchi Alain Barrat |
author_sort |
Teruyoshi Kobayashi |
title |
The structured backbone of temporal social ties |
title_short |
The structured backbone of temporal social ties |
title_full |
The structured backbone of temporal social ties |
title_fullStr |
The structured backbone of temporal social ties |
title_full_unstemmed |
The structured backbone of temporal social ties |
title_sort |
structured backbone of temporal social ties |
publisher |
Nature Publishing Group |
series |
Nature Communications |
issn |
2041-1723 |
publishDate |
2019-01-01 |
description |
Complex networks can be useful to describe social interactions but for large datasets one needs to identify significant links to extract information. While most existing methods work for static networks, here the authors propose a method to extract the backbone of significant links in temporal networks. |
url |
https://doi.org/10.1038/s41467-018-08160-3 |
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