Realistic network growth using only local information: From random to scale-free and beyond

dc.creatorSmith, David M. D.
dc.creatorLee, Chiu Fan
dc.creatorJohnson, Neil F.
dc.date2006-08-31
dc.date2006-09-04
dc.date.accessioned2026-07-07T07:19:55Z
dc.date.available2026-07-07T07:19:55Z
dc.descriptionWe introduce a simple one-parameter network growth algorithm which is able to reproduce a wide variety of realistic network structures but without having to invoke any global information about node degrees such as preferential-attachment probabilities. Scale-free networks arise at the transition point between quasi-random and quasi-ordered networks. We provide a detailed formalism which accurately describes the entire network range, including this critical point. Our formalism is built around a statistical description of the inter-node linkages, as opposed to the single-node degrees, and can be applied to any real-world network -- in particular, those where node-node degree correlations might be important.
dc.descriptionMinor typos corrected
dc.identifierhttps://arxiv.org/abs/cond-mat/0608733
dc.identifierhttp://arxiv.org/abs/cond-mat/0608733
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/114805
dc.subjectStatistical Mechanics
dc.subjectDisordered Systems and Neural Networks
dc.subjectPhysics and Society
dc.titleRealistic network growth using only local information: From random to scale-free and beyond
dc.typetext

Files

Collections