Poster - Functional Stable Limit in Random Connection Hypergraphs
Dec 9, 2025
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Abstract
We introduce a dynamic random hypergraph model constructed from a bipartite graph. In this model, both vertex sets of the bipartite graph are generated by marked Poisson point processes. Vertices of both vertex sets are equipped with marks representing their weight that influence their connection radii. Additionally, we also assign the vertices of the first vertex set a birth-death process with exponential lifetimes and the vertices of the second vertex set a time instant representing the occurrence of the corresponding vertices. Connections between vertices are established based on the marks and the birth-death processes, leading to a weighted dynamic hypergraph model featuring power-law degree distributions. We analyze the edge-count process in the challenging case of the heavy-tailed regime with infinite variance, we prove convergence to a novel stable process that is not Lévy and not even Markov.
Date
Dec 9, 2025 4:00 PM — 5:00 PM
Location
Aarhus University, iNANO auditorium
14 Gustav Wieds Vej, Aarhus C, 8000
Authors
Quantitative Researcher
I am a PhD researcher in Mathematics with experience in stochastic modeling, probabilistic analysis, and large-scale simulation, supported by Python/C++ model development.
Previously, I worked as a machine learning researcher at Bosch, where I developed and validated predictive models with a focus on uncertainty estimation and data-driven decision-making.
I am interested in applying quantitative methods to forecasting and risk modeling in energy and financial markets.