Learning the mechanisms of network growth

arXiv:2404.00793v1 Announce Type: cross
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