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Related Experiment Video

Updated: Sep 20, 2025

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

Quantification of network structural dissimilarities based on network embedding.

Zhipeng Wang¹, Xiu-Xiu Zhan¹, Chuang Liu¹

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

|June 9, 2022

Summary

This summary is machine-generated.

We developed a novel network comparison method using network embedding to capture global network structures. This approach effectively quantifies network dissimilarities, outperforming existing methods.

Keywords:

Computer science Network Network topology

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Area of Science:

Network Science
Graph Theory
Data Mining

Background:

Comparing network structures is crucial but challenging.
Existing methods using local features (shortest path, degree) miss global topological information.

Purpose of the Study:

To propose an efficient network comparison method leveraging network embedding.
To capture global structural information for improved network analysis.

Main Methods:

Constructing distance matrices from node embeddings (DeepWalk).
Defining network dissimilarity using Jensen-Shannon divergence of distance distributions.

Main Results:

The proposed method outperforms baseline methods on synthetic and empirical networks.
Demonstrated ability to distinguish between different network types.
Successfully captured network properties like average shortest path length and link density.

Conclusions:

The network embedding-based method provides a robust way to quantify network structural dissimilarities.
The approach is effective in distinguishing networks and capturing key network properties.