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Related Experiment Video

Updated: Jun 29, 2025

Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

A generalized simplicial model and its application.

Rongmei Yang¹, Fang Zhou^1,2, Bo Liu^1,2

¹Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China.

Chaos (Woodbury, N.Y.)

|April 4, 2024

Summary

This summary is machine-generated.

This study introduces a framework to analyze higher-order network structures and their impact on network functions. Regulating 2-simplices significantly alters network performance, offering new insights into complex systems.

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Area of Science:

Complex networks
Network science
Graph theory

Background:

Pairwise networks overlook crucial higher-order structural characteristics.
Research evaluating higher-order structures' effects on network functions remains limited.

Purpose of the Study:

To propose a framework for quantifying the impact of higher-order structures (e.g., 2-simplices) on complex network functions.
To develop a simplicial model for regulating 2-simplices while preserving the degree sequence.

Main Methods:

Developed a simplicial model to control the quantity of 2-simplices.
Maintained the original network's degree sequence during model construction.
Compared network performance using the original network and its simplicial model.

Main Results:

The framework effectively quantifies the influence of higher-order structures on network functions.
Regulating 2-simplices significantly impacts network performance across various functions.
The method indirectly controls higher-order simplices (beyond 2-order).

Conclusions:

The proposed framework is a general and effective tool for linking higher-order structures with network functions.
This approach deepens the understanding of micro-level structures' correlation with global network functions.
The framework serves as a valuable reference for diverse applications in network science.