Multi-species grandcanonical models for networks with reciprocity

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dc.creatorGarlaschelli, Diego
dc.creatorLoffredo, Maria I.
dc.date2005-06-20
dc.date.accessioned2026-07-07T07:38:39Z
dc.date.available2026-07-07T07:38:39Z
dc.descriptionReciprocity is a second-order correlation that has been recently detected in all real directed networks and shown to have a crucial effect on the dynamical processes taking place on them. However, no current theoretical model generates networks with this nontrivial property. Here we propose a grandcanonical class of models reproducing the observed patterns of reciprocity by regarding single and double links as Fermi particles of different `chemical species' governed by the corresponding chemical potentials. Within this framework we find interesting special cases such as the extensions of random graphs, the configuration model and hidden-variable models. Our theoretical predictions are also in excellent agreement with the empirical results for networks with well studied reciprocity.
dc.description4 pages, 1 figure
dc.identifierhttps://arxiv.org/abs/cond-mat/0506494
dc.identifierhttp://arxiv.org/abs/cond-mat/0506494
dc.identifierPhysical Review E 73, 015101(R) (2006)
dc.identifierdoi:10.1103/PhysRevE.73.015101
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/121165
dc.subjectDisordered Systems and Neural Networks
dc.subjectStatistical Mechanics
dc.subjectAdaptation and Self-Organizing Systems
dc.subjectData Analysis, Statistics and Probability
dc.titleMulti-species grandcanonical models for networks with reciprocity
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