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Related Experiment Video

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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

Assortative mixing in spatially-extended networks.

Vladimir V Makarov¹, Daniil V Kirsanov¹, Nikita S Frolov^1,2

¹REC 'Artificial Intelligence Systems and Neurotechnology', Yurij Gagarin State Technical University of Saratov, Polytechnicheskaja str 77, 410054, Saratov, Russia.

Scientific Reports

|September 16, 2018

Summary

This study models network transitions from short-range to scale-free structures. Disassortative mixing is essential for establishing long-range links in these evolving networks.

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Area of Science:

Network science
Graph theory
Complex systems

Background:

Spatially-extended networks transition from short-range to scale-free structures.
Scale-free networks exhibit heavy-tailed degree distributions.
Understanding network evolution is crucial in various scientific domains.

Purpose of the Study:

To introduce a model for generating graphs that transition from short-range to scale-free structures.
To investigate the relationship between network structure and degree-degree correlations during this transition.
To explore the role of assortativity and disassortativity in network evolution.

Main Methods:

Development of a graph generation model combining spatial growth and preferential attachment.
Analysis of degree-degree correlation properties during the transition to heterogeneous structures.
Comparison of model findings with experimental studies of neuronal cultures.

Main Results:

The transition to heterogeneous structures is accompanied by changes in degree-degree correlation properties.
High assortativity characterizes short-distance couplings in networks.
Low disassortativity is associated with long-range connectivity structures.

Conclusions:

Disassortative mixing is essential for establishing long-range links in spatially-extended networks.
The findings are consistent with experimental observations in 2D neuronal cultures.
The model provides insights into the mechanisms driving network structural changes.