Random networks with sublinear preferential attachment:degree evolutions


Dereich, S. and Morters, P., 2009. Random networks with sublinear preferential attachment:degree evolutions. Electronic Journal of Probability, 14, pp. 1222-1267.

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We define a dynamic model of random networks, where new vertices are connected to old ones with a probability proportional to a sublinear function of their degree. We first give a strong limit law for the empirical degree distribution, and then have a closer look at the temporal evolution of the degrees of individual vertices, which we describe in terms of large and moderate deviation principles. Using these results, we expose an interesting phase transition: in cases of strong preference of large degrees, eventually a single vertex emerges forever as vertex of maximal degree, whereas in cases of weak preference, the vertex of maximal degree is changing infinitely often. Loosely speaking, the transition between the two phases occurs in the case when a new edge is attached to an existing vertex with a probability proportional to the root of its current degree.


Item Type Articles
CreatorsDereich, S.and Morters, P.
Related URLs
Uncontrolled Keywordsdynamic random graphs,degree distribution,maximal degree,sublinear preferential attachment,moderate deviation principle,large deviation principle,barabasi-albert model
DepartmentsFaculty of Science > Mathematical Sciences
Research Centres
Centre for Mathematical Biology
ID Code14716
Additional InformationID number: Article Number 43


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