{ "id": "1509.07033", "version": "v1", "published": "2015-09-23T15:35:25.000Z", "updated": "2015-09-23T15:35:25.000Z", "title": "Random networks with preferential growth and vertex death", "authors": [ "Maria Deijfen" ], "journal": "Journal of Applied Probability 47, 1150-1163 (2010)", "categories": [ "math.PR" ], "abstract": "A dynamic model for a random network evolving in continuous time is defined where new vertices are born and existing vertices may die. The fitness of a vertex is defined as the accumulated in-degree of the vertex and a new vertex is connected to an existing vertex with probability proportional to a function $b$ of the fitness of the existing vertex. Furthermore, a vertex dies at a rate given by a function $d$ of its fitness. Using results from the theory of general branching processes, an expression for the asymptotic empirical fitness distribution $\\{p_k\\}$ is derived and analyzed for a number of specific choices of $b$ and $d$. When $b(i)=i+\\alpha$ and $d(i)=\\beta$ -- that is, linear preferential attachment for the newborn and random deaths -- then $p_k\\sim k^{-(2+\\alpha)}$. When $b(i)=i+1$ and $d(i)=\\beta(i+1)$, with $\\beta<1$, then $p_k\\sim (1+\\beta)^{-k}$, that is, if also the death rate is proportional to the fitness, then the power law distribution is lost. Furthermore, when $b(i)=i+1$ and $d(i)=\\beta(i+1)^\\gamma$, with $\\beta,\\gamma<1$, then $\\log p_k\\sim -k^\\gamma$ -- a stretched exponential distribution. The momentaneous in-degrees are also studied and simulations suggest that their behaviour is qualitatively similar to that of the fitnesses.", "revisions": [ { "version": "v1", "updated": "2015-09-23T15:35:25.000Z" } ], "analyses": { "keywords": [ "random network", "vertex death", "preferential growth", "existing vertex", "asymptotic empirical fitness distribution" ], "tags": [ "journal article" ], "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable", "adsabs": "2015arXiv150907033D" } } }