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
Event
Location
Aarhus University, iNANO auditorium
14 Gustav Wieds Vej, Aarhus C, 8000
Authors
Quantitative Researcher
Quantitative researcher with a PhD in Mathematics, combining expertise in stochastic modeling, statistical inference, and machine learning with industrial experience in developing predictive systems.
Experienced in large-scale time-series analysis, uncertainty quantification, and rigorous model validation, with strong Python/C++ skills and a proven track record of translating research into production software.
Currently developing intraday electricity-price models and stochastic-control methods for optimal liquidation at Aarhus University, in collaboration with an energy trading firm.