Generating fine-grained surrogate temporal networks - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Communications Physics Année : 2024

Generating fine-grained surrogate temporal networks

Résumé

Temporal networks are essential for modeling and understanding time-dependent systems, from social interactions to biological systems. However, real-world data to construct meaningful temporal networks are expensive to collect or unshareable due to privacy concerns. Generating arbitrarily large and anonymized synthetic graphs with the properties of real-world networks, namely surrogate networks, is a potential way to bypass the problem. However, it is not easy to build surrogate temporal networks which do not lack information on the temporal and/or topological properties of the input network and their correlations. Here, we propose a simple and efficient method that decomposes the input network into starlike structures evolving in time, used in turn to generate a surrogate temporal network. The model is compared with state-of-the-art models in terms of similarity of the generated networks with the original ones, showing its effectiveness and its efficiency in terms of execution time. The simplicity of the algorithm makes it interpretable, extendable and scalable.
Fichier principal
Vignette du fichier
s42005-023-01517-1.pdf (2.18 Mo) Télécharger le fichier
fig1.pdf (218.21 Ko) Télécharger le fichier
fig2.pdf (263.92 Ko) Télécharger le fichier
fig3.pdf (723.89 Ko) Télécharger le fichier
fig4.pdf (112.61 Ko) Télécharger le fichier
fig5.pdf (164.68 Ko) Télécharger le fichier
fig6.pdf (220.25 Ko) Télécharger le fichier
fig7.pdf (277.87 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Licence : CC BY - Paternité
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04404147 , version 1 (18-01-2024)

Licence

Paternité

Identifiants

Citer

A. Longa, G. Cencetti, S. Lehmann, A. Passerini, B. Lepri. Generating fine-grained surrogate temporal networks. Communications Physics, 2024, 7 (1), pp.22. ⟨10.1038/s42005-023-01517-1⟩. ⟨hal-04404147⟩
15 Consultations
3 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More