Effects of Preference for Attachment to Low-degree Nodes on the Degree Distributions of a Growing Directed Network and a Simple Food-Web Model

dc.creatorSevim, Volkan
dc.creatorRikvold, Per Arne
dc.date2005-10-27
dc.date2006-03-15
dc.date.accessioned2026-07-07T06:45:37Z
dc.date.available2026-07-07T06:45:37Z
dc.descriptionWe study the growth of a directed network, in which the growth is constrained by the cost of adding links to the existing nodes. We propose a new preferential-attachment scheme, in which a new node attaches to an existing node i with probability proportional to 1/k_i, where k_i is the number of outgoing links at i. We calculate the degree distribution for the outgoing links in the asymptotic regime (t->infinity), both analytically and by Monte Carlo simulations. The distribution decays like k c^k/Gamma(k) for large k, where c is a constant. We investigate the effect of this preferential-attachment scheme, by comparing the results to an equivalent growth model with a degree-independent probability of attachment, which gives an exponential outdegree distribution. Also, we relate this mechanism to simple food-web models by implementing it in the cascade model. We show that the low-degree preferential-attachment mechanism breaks the symmetry between in- and outdegree distributions in the cascade model. It also causes a faster decay in the tails of the outdegree distributions for both our network growth model and the cascade model.
dc.description10 pages, 7 figures. A new figure added. Minor modifications made in the text
dc.identifierhttps://arxiv.org/abs/cond-mat/0510763
dc.identifierhttp://arxiv.org/abs/cond-mat/0510763
dc.identifierPhys. Rev. E 73, 056115 (2006)
dc.identifierdoi:10.1103/PhysRevE.73.056115
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/103094
dc.subjectStatistical Mechanics
dc.subjectPopulations and Evolution
dc.titleEffects of Preference for Attachment to Low-degree Nodes on the Degree Distributions of a Growing Directed Network and a Simple Food-Web Model
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