Research output per year
Research output per year
Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia, Nelly Litvak*
Research output: Contribution to journal › Article › Academic › peer-review
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.
Original language | English |
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Article number | 11866 |
Journal | Scientific reports |
Volume | 14 |
Issue number | 1 |
DOIs | |
Publication status | Published - May 2024 |
Research output: Working paper › Preprint › Academic