Poster - Topological Data Analysis of Higher-Order Networks
Jun 26, 2023
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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
Jun 26, 2023 5:00 PM — 6:00 PM
Event
Location
Aalborg University
25 Thomas Manns Vej, Aalborg, 9220
Authors
Quantitative Researcher
Quantitative Researcher with a PhD in Mathematics, specializing in stochastic modeling, machine learning, and predictive systems for financial markets.
Experienced in probabilistic modeling, Monte Carlo simulation, uncertainty quantification, and statistical validation for data-driven decision-making.
Currently developing intraday energy-market price prediction models and optimal liquidation strategies using machine learning, functional data analysis, and stochastic differential equations.
Interested in market prediction problems where model quality is directly reflected in trading performance and PnL.