Applications of Spectral Graph Theory in Machine Learning and Data Science
K M V Ramana, R Shireesha
Spectral Graph Theory (SGT) has emerged as a powerful mathematical framework for analyzing graphstructured 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.
