Disentangling complex systems: IdopNetwork meets GLMY homology theory

The study of complex systems has captured widespread attention in recent years, emphasizing the exploration of interactions and emergent properties among system units.Network analysis based on graph theory has emerged as a powerful approach for analyzing network topology and functions, making them widely adopted in complex systems.IdopNetwork is an advanced statistical physics framework that constructs the interaction within complex systems by integrating large-scale omics data.By combining GLMY theory, the structural characteristics of the network topology can be traced, providing deeper insights into the dynamic evolution of the network.This combination not only offers a novel perspective for dissecting the internal regulation of complex systems from a holistic standpoint but also provides significant support for applied fields such as data science, complex disease, and materials science.

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Publication Details

Journal
Data Analytics and Topology
Published
2025-01-01
DOI
https://doi.org/10.4310/daat.250513012010
Primary Topic
Topological and Geometric Data Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Disentangling complex systems: IdopNetwork meets GLMY homology theory

Shuang Wu, Mengmeng Zhang
Data Analytics and Topology
Topological and Geometric Data Analysis
article

Disentangling complex systems: IdopNetwork meets GLMY homology theory

Shuang Wu, Mengmeng Zhang
article en

Abstract

The study of complex systems has captured widespread attention in recent years, emphasizing the exploration of interactions and emergent properties among system units.Network analysis based on graph theory has emerged as a powerful approach for analyzing network topology and functions, making them widely adopted in complex systems.IdopNetwork is an advanced statistical physics framework that constructs the interaction within complex systems by integrating large-scale omics data.By combining GLMY theory, the structural characteristics of the network topology can be traced, providing deeper insights into the dynamic evolution of the network.This combination not only offers a novel perspective for dissecting the internal regulation of complex systems from a holistic standpoint but also provides significant support for applied fields such as data science, complex disease, and materials science.

Data Analytics and TopologyVol. 1(1)
Division of Mathematical Sciences
Reduced inequalities
Openalex Percentile: Top 85%
Topological and Geometric Data Analysis
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