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This is a Quantitative Study study.

Spectral Graph Theory for Community Detection in Large-Scale Social Networks: A Mathematical Framework

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This analysis applies spectral graph theory to enhance community detection in social networks, suggesting new computational efficiencies.

The method achieves high accuracy with normalized mutual information of 0.77 and adjusted rand index of 0.72.
Using the spectral gap for estimating group numbers, the framework efficiently processes networks with over one hundred thousand nodes.
The study validates the approach on the MUSAE GitHub dataset, which features node characteristics and ground-truth communities.
The framework integrates sparse linear algebra for improved computational performance, suggesting future work in temporal graphs.

Salil Goel (2025) studied this question.

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