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Directed Network Comparison Using Motifs.

Chenwei Xie¹, Qiao Ke¹, Haoyu Chen¹

¹Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China.

Entropy (Basel, Switzerland)

|February 23, 2024

Summary

This summary is machine-generated.

This study introduces a new method for comparing directed networks using motifs. It effectively captures local, global, and higher-order network differences, outperforming existing approaches.

Keywords:

Jensen–Shannon divergence directed networks motifs network comparison

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Area of Science:

Network Science
Graph Theory
Data Analysis

Background:

Comparing networks is crucial but challenging, especially for directed networks.
Existing methods often overlook directionality and higher-order network attributes.
Many real-world networks (biological, social, transportation) are inherently directed.

Purpose of the Study:

To develop a motif-based method for comparing directed networks.
To capture local, global, and higher-order network differences.
To address limitations of existing topological comparison methods.

Main Methods:

Constructing node-specific motif distribution vectors.
Utilizing Jensen-Shannon divergence to quantify network dissimilarity.
Applying the method to real-world directed networks and their null/perturbed models.

Main Results:

The proposed method effectively captures diverse network differences.
Demonstrated superiority over state-of-the-art baseline methods.
Showcased robustness across various parameter settings.

Conclusions:

The motif-based approach offers a powerful tool for directed network comparison.
This method enhances understanding of complex network structures.
It provides a robust and versatile framework for network analysis.