Small-World Propensity and Weighted Brain Networks.

Area of Science:

• Network Science
• Computational Neuroscience
• Systems Biology

Background:

• Network structure provides fundamental insights into complex systems' function.
• Small-world networks, characterized by high clustering and short path lengths, facilitate information flow.
• Existing small-worldness metrics are limited by network density and connection strength.

Purpose of the Study:

• To develop a novel, density-independent metric for quantifying small-worldness.
• To extend this metric to weighted networks, specifically brain networks.
• To provide a comprehensive toolbox for assessing brain network architecture.

Main Methods:

• Developed the Small-World Propensity (SWP) metric for unbiased binary network assessment.
• Created a standardized procedure for generating weighted small-world networks.
• Developed a weighted SWP extension and a method for mapping empirical brain network data.

Main Results:

• The SWP metric offers an unbiased assessment of small-world structure across varying network densities.
• The weighted SWP effectively quantifies small-worldness in weighted brain networks.
• The C. elegans neuronal network, a canonical small-world example, exhibits surprisingly low SWP.

Conclusions:

• The SWP provides a robust tool for analyzing network topology, overcoming limitations of existing methods.
• The developed toolbox enables accurate assessment and comparison of brain network architectures.
• Findings challenge conventional understanding of established biological small-world networks.