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Related Experiment Video

Updated: May 31, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Persistent Homology Combined with Machine Learning for Social Network Activity Analysis.

Zhijian Zhang^1,2, Yuqing Sun¹, Yayun Liu¹

¹Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China.

Entropy (Basel, Switzerland)

|January 24, 2025

Summary

This study introduces a novel method to classify social network users by analyzing their activity levels. By measuring topological complexity using persistence entropy, researchers can effectively group users, enhancing our understanding of online behavior.

Keywords:

clustering machine learning persistent entropy persistent homology social networks

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Area of Science:

Social Network Analysis
Computational Topology
Machine Learning

Background:

Social media facilitates frequent user communication.
Understanding user behavior through activity classification is crucial.
Existing methods may not fully capture complex network dynamics.

Purpose of the Study:

To develop a novel method for classifying social network users based on their activity.
To quantify user activity using topological features of ego networks.
To enhance the understanding of user behavior in social networks.

Main Methods:

Constructing ego networks for individual users.
Encoding topological features using persistence diagrams and persistence homology.
Computing persistence entropy and defining Norm Entropy (NE(X)) to measure topological complexity and user activity.
Training machine learning models with extracted feature vectors for user classification.

Main Results:

The proposed Norm Entropy (NE(X)) effectively represents the topological complexity and activity level of social network nodes.
Numerical experiments demonstrate the algorithm's capability in effectively classifying users into distinct groups.
The method shows promising results in evaluating clustering quality using metrics like profile coefficients.

Conclusions:

The developed algorithm provides an effective approach for classifying social network users.
This classification based on topological complexity offers a strong foundation for future research and applications in social network analysis.
The study highlights the potential of applying persistence homology to understand complex network structures and user behavior.