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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

An Event-Link Network Model Based on Representation in P-Space.

Wenjun Zhang¹, Xiangna Chen^2,3, Weibing Deng³

¹School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei 230012, China.

Entropy (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

This study introduces an event-link model to generate complex networks in P-space, overcoming previous challenges. The model accurately replicates real-world network properties like small-world behavior and scale-free structure.

Keywords:

complex network event–link model growing mechanism representation in P-space transport network

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Area of Science:

Complex network analysis
Network modeling
Data science

Background:

L-space and P-space are key representations for complex networks.
Generating networks in P-space is challenging for existing models.
Understanding P-space network mechanisms is crucial.

Purpose of the Study:

Empirically analyze node distribution and network properties in P-space.
Propose a novel event-link model for P-space network generation.
Investigate the operational mechanisms of real-world networks in P-space.

Main Methods:

Empirical analysis of node distribution in P-space.
Development of an event-link model using real transportation network data.
Simulation experiments to evaluate topological features and parameter effects.

Main Results:

The event-link model successfully generates networks in P-space.
Simulations align with theoretical analysis.
Generated networks exhibit small-world, scale-free properties, and high clustering.

Conclusions:

The event-link model effectively generates networks with stable structures resembling real-world data.
Adjustable parameters allow flexible control over network growth and evolution.
The model provides insights into P-space network mechanisms.