These are exciting times for quantum physics as new quantum technologies are expected to
soon transform computing at an unprecedented level. Simultaneously network science is …

We consider models of evolving networks G n: n≥ 0 modulated by two parameters: an
attachment function f: N 0→ R+ and a (possibly random) attachment sequence mi: i≥ 1 …

We study a general model of recursive trees where vertices are equipped with independent
weights and at each time-step a vertex is sampled with probability proportional to its fitness …

Preferential attachment models form a popular class of growing networks, where incoming
vertices are preferably connected to vertices with high degree. We consider a variant of this …

We introduce a model of evolving preferential attachment trees where vertices are assigned
weights, and the evolution of a vertex depends not only on its own weight, but also on the …

In this paper we initiate the theory of Crump-Mode-Jagers branching processes (BP) in the
setting where no Malthusian parameter exist, ie, the process grows faster than exponential …

UPPER LARGE DEVIATIONS FOR POWER-WEIGHTED EDGE LENGTHS IN SPATIAL
RANDOM NETWORKS Page 1 Adv. Appl. Probab. 56, 34–70 (2024) doi:10.1017/apr.2023.10 …

We study the evolution of the graph distance and weighted distance between two fixed
vertices in dynamically growing random graph models. More precisely, we consider …

We introduce a model of a preferential attachment based random graph which extends the
family of models in which condensation phenomena can occur. Each vertex has an …

A generalisation of Kingman's model of selection and mutation has been made in a previous
paper which assumes all mutation probabilities to be iid. The weak convergence of fitness …