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Related Experiment Video

Updated: Mar 8, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Community structure unveils the path multiplicity in complex networks.

Ye Deng^1,2, Jun Wu^3,4, Xin Lu⁵

¹Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, China.

Nature Communications

|March 6, 2026

Summary

This summary is machine-generated.

Community structure significantly influences path multiplicity in complex networks. This finding reveals an interface-driven effect that increases shortest paths, aiding network design.

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Area of Science:

Network Science
Complex Systems Analysis
Computational Social Science

Background:

Real-world networks exhibit significant heterogeneity in shortest paths between nodes.
The underlying mechanisms driving this path multiplicity remain largely unexplored.
Understanding path multiplicity is crucial for network analysis and optimization.

Purpose of the Study:

To identify key factors influencing path multiplicity in complex networks.
To introduce and analyze the concept of relative path multiplicity.
To develop a network model that replicates observed path multiplicity phenomena.

Main Methods:

Introduction of relative path multiplicity metric.
Correlation analysis between community structure and path multiplicity.
Targeted edge-rewiring experiments to validate findings.
Development of a tribal-structure-based network model.

Main Results:

Community structure is strongly correlated with path multiplicity, outperforming other network metrics.
An interface-driven effect at community boundaries sharply increases the number of shortest paths.
The proposed tribal-structure model successfully reproduces real-world network phenomena.

Conclusions:

Community structure is a primary driver of path multiplicity in complex networks.
The findings offer a mechanistic explanation for path heterogeneity.
This research has implications for network design, optimization, and understanding complex systems.