backbone: An R package to extract network backbones.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2022
Historique:
received: 16 03 2022
accepted: 13 05 2022
entrez: 31 5 2022
pubmed: 1 6 2022
medline: 1 6 2022
Statut: epublish

Résumé

Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.

Identifiants

pubmed: 35639738
doi: 10.1371/journal.pone.0269137
pii: PONE-D-22-07882
pmc: PMC9154188
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0269137

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Zachary P Neal (ZP)

Psychology Department, Michigan State University, East Lansing, MI, United States of America.

Classifications MeSH