Learning the mechanisms of network growth
<|reference_start|>Learning the mechanisms of network growth: 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.<|reference_end|>
@article{touwen2024learning,
    title={Learning the mechanisms of network growth},
    author={Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro
  Garavaglia, Nelly Litvak},
    journal={Scientific Reports, volume 14, Article number: 11866 (2024)},
    year={2024},
    doi={10.1038/s41598-024-61940-4 10.1038/s41598-024-61940-4},
    archivePrefix={arXiv},
    eprint={2404.00793},
    primaryClass={cs.SI math.PR stat.ML}
}