Spectral Methods in Combinatorics
Spectral graph theory uses eigenvalues of matrices associated with graphs to derive combinatorial properties. The adjacency matrix, Laplacian, and normalized Laplacian encode structural information that can be extracted through linear algebra.
Adjacency and Laplacian Eigenvalues
For a graph on vertices, the adjacency matrix has eigenvalues , called the spectrum of . The Laplacian , where is the diagonal degree matrix, has eigenvalues .
For a -regular graph with second eigenvalue and vertex sets : where counts edges between and . Small implies that the edge distribution is close to that of a random graph.
The Paley graph on (where ) has vertices and edges when is a quadratic residue. It is -regular with second eigenvalue . The expander mixing lemma then guarantees that this graph is a strong expander.
Hoffman's Bound
For a -regular graph on vertices with smallest eigenvalue , the independence number satisfies:
The Kneser graph has vertices and edges between disjoint sets. It is regular with degree and eigenvalues for . Hoffman's bound applied to gives , matching the Erdos-Ko-Rado bound.
The spectral gap of a -regular graph controls its expansion properties. The Alon-Boppana bound states that for any family of -regular graphs with . Graphs achieving are called Ramanujan graphs and have optimal expansion properties.
Applications to Chromatic Number
For a graph with largest eigenvalue and smallest eigenvalue : This spectral lower bound for the chromatic number is tight for Kneser graphs and many other structured graphs.