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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Main Authors: Teruyoshi Kobayashi, Taro Takaguchi, Alain Barrat
Format: Article
Language:English
Published: Nature Publishing Group 2019-01-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-018-08160-3
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spelling 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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