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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Cycle Based Network Centrality.

Xiaoping Zhou^1,2, Xun Liang³, Jichao Zhao⁴

¹School of Information, Renmin University of China, Beijing, 100872, China.

Scientific Reports

|August 8, 2018

Summary

This summary is machine-generated.

Shortest paths in networks are incomplete for centrality measures. This study introduces robust cycle-based centrality measures, shortest cycle closeness centrality (SCC) and all cycle betweenness centrality (ACC), to improve network analysis.

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Area of Science:

Network Science
Graph Theory
Data Analysis

Background:

Paths, especially shortest paths, are fundamental in network analysis.
Current network centrality measures often rely on shortest paths, potentially limiting their accuracy.
Research on cycle-based network centrality is limited compared to path-based approaches.

Purpose of the Study:

To demonstrate the informational incompleteness of shortest paths in network centrality.
To explore the relationship between shortest cycles and shortest paths in real-world networks.
To propose novel cycle-based centrality measures for improved network analysis.

Main Methods:

Analysis of shortest path and shortest cycle relationships in extensive real-world networks.
Development of two new centrality measures: shortest cycle closeness centrality (SCC) and all cycle betweenness centrality (ACC).

Main Results:

Shortest paths provide incomplete information for network centrality.
Shortest cycles are more robust than shortest paths for network analysis.
The proposed SCC and ACC measures effectively address the limitations of path-based centrality.

Conclusions:

Cycle-based network centrality offers a more robust alternative to path-based measures.
The findings highlight the need to consider cycles for a comprehensive understanding of network structures.
The proposed measures have potential applications beyond centrality, improving path-based analyses in various network science scenarios.