Clustering based on eigenvectors of the adjacency matrix - Lucińska, Małgorzata; Wierzchoń, Sławomir T. (Zielona Góra: Uniwersytet Zielonogórski)
The paper presents a novel spectral algorithm EVSA (eigenvector structure analysis), which uses eigenvalues and eigenvectors of the adjacency matrix in order to discover clusters. Based on matrix perturbation theory and properties of graph spectra we show that the adjacency matrix can be more suitable for partitioning than other Laplacian matrices. The main problem concerning the use of the adjacency matrix is the selection of the appropriate eigenvectors.
Keywords: spectral clustering; adjacency matrix eigenvalues/eigenvectors; graph perturbation theory; eigengap heuristics