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arXiv · 2602.13673

Data-driven macroscopic dynamics of complex networks using Topological Data Analysis and the Equation-Free Method

Konstantinos Spiliotis·Ole Sönnerborn·Haralampos Hatzikirou·Nikos I. Kavallaris
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Abstract

In this work, we present a computational framework for exploring and analyzing the macroscopic dynamics of complex agent-based network models by integrating Topological Data Analysis with the Equation-Free Method. To demonstrate the effectiveness of our method, we apply it to Erd\H{o}s--R\'enyi-type random networks. Central to our approach is a Topological Data Analysis-based filtration process driven by the density of activated network nodes (agents), from which we extract a coarse-grained macroscopic topological observable. This observable is defined via persistent Betti numbers, thus requiring significantly reduced data dimensionality while retaining essential topological features. Subsequently, within the Equation-Free Method framework, we show firstly that a \textit{lifting procedure} can be achieved using topological properties and secondly, a data-driven evolution law that governs the dynamics of this macroscopic variable. Finally, we perform a numerical bifurcation and stability analysis to investigate the global behavior and qualitative transitions of the emergent macroscopic dynamics.

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Konstantinos Spiliotis, Ole Sönnerborn, Haralampos Hatzikirou, Nikos I. Kavallaris. 2026-02-14. Data-driven macroscopic dynamics of complex networks using Topological Data Analysis and the Equation-Free Method. https://arxiv.org/abs/2602.13673

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