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Improving the Walktrap Algorithm Using K-Means Clustering.

Michael Brusco¹, Douglas Steinley², Ashley L Watts³

¹Business Analytics, Florida State University.

Multivariate Behavioral Research

|February 16, 2024

Summary

This summary is machine-generated.

The walktrap algorithm, popular in psychological research for community detection, can be improved. An alternative using K-means clustering offers better solutions for sum-of-squares optimization problems.

Keywords:

K-means clustering Psychological networks community detection hierarchical clustering walktrap algorithm

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Area of Science:

Psychological research
Network analysis
Computational methods

Background:

The walktrap algorithm is a widely used community-detection method in psychology.
It relies on hierarchical clustering, which may not be optimal for typical psychological network sizes.
Existing methods can struggle with sum-of-squares optimization in community detection.

Purpose of the Study:

To present a computationally simpler alternative to the walktrap algorithm.
To demonstrate that K-means clustering can provide superior solutions to the sum-of-squares optimization problem.
To evaluate the impact of improved sum-of-squares solutions on community detection.

Main Methods:

Developed a computational alternative to hierarchical clustering for community detection.
Applied exact and approximate K-means clustering methods to solve the sum-of-squares optimization problem.
Conducted three simulation studies and analyzed empirical networks.

Main Results:

The K-means clustering approach provides better solutions to the sum-of-squares optimization problem compared to hierarchical clustering used in walktrap.
The alternative method is conceptually easier to understand.
Empirical analyses and simulations confirm the benefits of the proposed approach.

Conclusions:

K-means clustering offers a more effective computational alternative for community detection in psychological research.
The proposed method improves upon the walktrap algorithm by providing better optimization solutions.
This work facilitates more accurate community detection in psychological networks.