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Learning the mechanisms of network growth
April 2, 2024, 7:50 p.m. | Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia, Nelly Litvak
stat.ML updates on arXiv.org arxiv.org
Abstract: We propose a novel model-selection method for dynamic real-life 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 …
abstract art arxiv classifier cs.si data dynamic generated graph growth life math.pr network networks novel random state stat.ml synthetic training type
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