Poster - Topological Data Analysis of Higher-Order Networks
Apr 4, 2025
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0 min read
Abstract
Preferential attachment is a popular mechanism for generating scale-free networks. While it offers a compelling narrative, the underlying reinforced processes make it difficult to rigorously establish subtle properties. Recently, age-dependent random connection models were proposed as an alternative that are capable of generating similar networks with a mechanism that is amenable to a more refined analysis. In this poster, we analyze the asymptotic behavior of higher-order topological characteristics such as higher-order degree distributions and Betti numbers in large domains.
Date
Apr 4, 2025 12:00 PM — 1:30 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.