Applications of Spectral Graph Theory in Machine Learning and Data Science

Abstract

Spectral Graph Theory (SGT) has emerged as a powerful mathematical framework for analyzing graph structured data, with significant applications in machine learning and data science. This study explores the role of spectral methods in key machine learning tasks, including spectral clustering, graph neural networks (GNNs), dimensionality reduction using Laplacian Eigenmaps, semi-supervised learning, and graph-based anomaly detection. Experimental evaluations demonstrate that GNNs achieve the highest accuracy (92.8%) in node classification, while spectral clustering effectively partitions complex datasets (89.2% accuracy). Laplacian Eigenmaps offer an efficient dimensionality reduction technique (87.5% accuracy with the lowest computational time of 9.3s), making it suitable for high-dimensional data processing. Furthermore, graph-based anomaly detection outperforms other methods (94.1% accuracy) in detecting network intrusions, highlighting the utility of spectral properties in cybersecurity. The results emphasize the efficiency and interpretability of spectral approaches in handling graph-based machine learning problems. This study provides insights into the computational trade-offs of different spectral techniques and suggests future research directions in hybrid models integrating deep learning and spectral graph analysis.