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Learning the mechanisms of network growth

Touwen, Lourens ; Bucur, Doina ; van der Hofstad, Remco ; Garavaglia, Alessandro ; Litvak, Nelly

Scientific reports, 2024-05, Vol.14 (1), p.11866-11866, Article 11866 [Periódico revisado por pares]

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Título:
Learning the mechanisms of network growth
Autor: Touwen, Lourens ; Bucur, Doina ; van der Hofstad, Remco ; Garavaglia, Alessandro ; Litvak, Nelly
Assuntos: Classification
É parte de: Scientific reports, 2024-05, Vol.14 (1), p.11866-11866, Article 11866
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptually novel type of dynamic features that count new links received by a group of vertices in a particular time interval. The proposed features are easy to compute, analytically tractable, and interpretable. Our approach achieves a near-perfect classification of synthetic networks, exceeding the state-of-the-art by a large margin. Applying our classification method to real-world citation networks gives credibility to the claims in the literature that models with preferential attachment, fitness and aging fit real-world citation networks best, although sometimes, the predicted model does not involve vertex fitness.
Editor: England: Nature Publishing Group
Idioma: Inglês

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