Learning the mechanisms of network growth

• URL: http://arxiv.org/abs/2404.00793v3
• Date: Mon, 27 May 2024 17:52:07 GMT
• Title: Learning the mechanisms of network growth
• Authors: Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia, Nelly Litvak,
• Abstract summary: We propose a novel model-selection method for dynamic networks. Data is generated by simulating nine state-of-the-art random graph models. Proposed features are easy to compute, analytically tractable, and interpretable.
• Score: 42.1340910148224
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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