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Related Experiment Video

Updated: Jul 3, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Disorder and decision cost in spatial networks.

Massimiliano Zanin¹, Javier M Buldú, P Cano

¹Universidad Autonoma de Madrid, 28049 Cantoblanco, Madrid, SpainDepartamento de Fisica, Universidad Rey Juan Carlos, Tulipan s/n, 28933 Mostoles, Madrid, Spain.

Chaos (Woodbury, N.Y.)

|July 8, 2008

Summary

We introduce decision cost, a measure of spatial network disorder considering node positions. Normalization allows comparing disorder across networks of varying sizes, revealing insights into real-world systems like airport connections.

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Area of Science:

Network Science
Graph Theory
Spatial Analysis

Background:

Understanding network structure and disorder is crucial in various scientific fields.
Existing methods often overlook the spatial embedding of network nodes.
Quantifying disorder in spatial networks requires novel approaches.

Purpose of the Study:

Introduce the concept of decision cost for spatial graphs.
Develop a method to quantify network disorder considering node positions.
Evaluate the influence of network size and enable cross-size comparisons.

Main Methods:

Define decision cost as a measure of spatial network disorder.
Analyze the impact of network size on decision cost.
Normalize decision cost to compare disorder across networks of different sizes.
Apply the framework to analyze disorder in airport networks.

Main Results:

Decision cost quantifies network disorder incorporating spatial information.
Network size influences decision cost; normalization is key for comparison.
Analysis of airport networks reveals differences in spatial disorder between countries.

Conclusions:

The decision cost framework provides a robust measure of spatial network disorder.
Normalized decision cost allows meaningful comparisons of disorder across networks of varying scales.
The concept is extendable to higher dimensions and networks with node fitness properties.