"Stealing fire or stacking knowledge" by machine intelligence to model link prediction in complex networks.
Artificial intelligence
Network
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
20 Jan 2023
20 Jan 2023
Historique:
received:
15
04
2022
revised:
15
09
2022
accepted:
22
11
2022
entrez:
26
12
2022
pubmed:
27
12
2022
medline:
27
12
2022
Statut:
epublish
Résumé
Current methodologies to model connectivity in complex networks either rely on network scientists' intelligence to discover reliable physical rules or use artificial intelligence (AI) that stacks hundreds of inaccurate human-made rules to make a new one that optimally summarizes them together. Here, we provide an accurate and reproducible scientific analysis showing that, contrary to the current belief, stacking more good link prediction rules does not necessarily improve the link prediction performance to nearly optimal as suggested by recent studies. Finally, under the light of our novel results, we discuss the pros and cons of each current state-of-the-art link prediction strategy, concluding that none of the current solutions are what the future might hold for us. Future solutions might require the design and development of next generation "creative" AI that are able to generate and understand complex physical rules for us.
Identifiants
pubmed: 36570772
doi: 10.1016/j.isci.2022.105697
pii: S2589-0042(22)01970-8
pmc: PMC9771718
doi:
Types de publication
Journal Article
Langues
eng
Pagination
105697Informations de copyright
© 2022 The Authors.
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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