Learning the mechanisms of network growth.

Autor: Touwen L; Department of Mathematics and Computer Science, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands., Bucur D; Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands., van der Hofstad R; Department of Mathematics and Computer Science, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands., Garavaglia A; Department of Mathematics and Computer Science, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands., Litvak N; Department of Mathematics and Computer Science, Eindhoven University of Technology, Groene Loper 3, 5612 AE, Eindhoven, The Netherlands. n.v.litvak@tue.nl.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 May 24; Vol. 14 (1), pp. 11866. Date of Electronic Publication: 2024 May 24.
DOI: 10.1038/s41598-024-61940-4
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz: