We investigate a class of growing graphs embedded into the $d$-dimensional torus where new vertices arrive according to a Poisson process in time, are randomly placed in space and connect to existing vertices with a probability depending on time, their spatial distance and their relative ages. This simple model for a scale-free network is called the age-based spatial preferential attachment network and is based on the idea of preferential attachment with spatially induced clustering. We show that the graphs converge weakly locally to a variant of the random connection model, which we call the age-dependent random connection model. This is a natural infinite graph on a Poisson point process where points are marked by a uniformly distributed age and connected with a probability depending on their spatial distance and both ages. We use the limiting structure to investigate asymptotic degree distribution, clustering coefficients and typical edge lengths in the age-based spatial preferential attachment network.
Long-time behaviour of a multidimensional age-dependent branching process with a singular jump kernel modelling telomere shortening
Institut de Mathématiques de Toulouse, CNRS, Université de Toulouse（图卢兹数学研究所）；CMAP, CNRS, INRIA, École polytechnique, Institut Polytechnique de Paris（巴黎理工学院高等应用数学中心）
