A growing social network model in geographical space

Détails

ID Serval
serval:BIB_5402D953C9D7
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
A growing social network model in geographical space
Périodique
Journal of Statistical Mechanics: Theory and Experiment
Auteur(s)
Antonioni A., Tomassini M.
ISSN
1742-5468
Statut éditorial
Publié
Date de publication
19/09/2017
Peer-reviewed
Oui
Volume
2017
Numéro
9
Pages
093403
Langue
anglais
Résumé
In this work we propose a new model for the generation of social networks that includes their often ignored spatial aspects. The model is a growing one and links are created either taking space into account, or disregarding space and only considering the degree of target nodes. These two effects can be mixed linearly in arbitrary proportions through a parameter. We numerically show that for a given range of the combination parameter, and for given mean degree, the generated network class shares many important statistical features with those observed in actual social networks, including the spatial dependence of connections. Moreover, we show that the model provides a good qualitative fit to some measured social networks.
Mots-clé
socio-economic networks, random graphs, Statistics, Probability and Uncertainty, Statistics and Probability, Statistical and Nonlinear Physics
Création de la notice
21/09/2017 16:03
Dernière modification de la notice
20/08/2019 15:09
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