Disentangling Complex Systems: IdopNetwork Meets GLMY Homology Theory

TL;DR

This paper introduces a novel approach combining IdopNetwork, a statistical physics framework, with GLMY homology theory to analyze complex systems, revealing insights into their topology and evolution from a holistic perspective.

Contribution

It presents an innovative integration of IdopNetwork with GLMY homology theory, offering a new method for dissecting complex system regulation and dynamics.

Findings

Enhanced understanding of network topology and evolution.

New methodological framework for complex system analysis.

Potential applications in data science and materials science.

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.