Spectral Graph Theory studies the properties of graphs through the eigenvalues and eigenvectors of matrices associated with the graph, such as the adjacency matrix or the Laplacian matrix. It provides insights into graph connectivity, expansion, and can be used in applications like network analysis, machine learning, and computer vision.