Spectral Graph Clustering

Spectral Graph Clustering is a clustering method based on spectral theory, which achieves effective partitioning of complex network structures by analyzing the eigenvalues and eigenvectors of the graph's Laplacian matrix. Its goal is to identify sets of nodes in the graph that have similar properties, thereby revealing underlying community structures or functional modules. This method has significant application value in social network analysis, bioinformatics, image segmentation, and other fields, providing deep insights into data structure and optimized solutions.