Conference Agenda

Recently, the class of weight-dependent random connection models has received substantial research interest. This is because such networks are promising candidates for modeling complex networks. They are capable of reproducing heavy-tailed degree distributions while avoiding the tree-like structure of combinatorial random graph models. This motivates the need to derive limit results for standard network functions such as edge counts, triangle counts or more general subgraph counts.

While the theory of normal approximation in spatial random networks and stochastic geometry has seen several breakthroughs in the recent years, only some isolated results regarding α-stable convergence could be established. Making use of tools from extreme value theory, in this talk, I will illustrate how such $\alpha$-stable results can be established for different variants of weight-dependent random connection models. If time permits, I will also comment on results in the dynamic case, when the networks changes over time.

I will conclude by illustrating some of the results through collaboration data from the arxiv network.

This talk is based on a series of (partly ongoing) joint works with M. Brun, B. Jahnel, P. Juhász, M. Otto, and T. Owada.