ConceptComplete

Spectral Techniques in Combinatorics

Eigenvalue methods provide powerful tools for proving combinatorial results that seem purely structural. The interplay between algebraic properties of graph matrices and combinatorial structure is a central theme of modern graph theory.


The Expander Mixing Lemma

Definition8.6Expander Mixing Lemma

For a dd-regular graph GG with second eigenvalue λ=λ2(A)\lambda = \lambda_2(A): for all vertex sets S,TVS, T \subseteq V, e(S,T)dSTnλST\left|e(S, T) - \frac{d|S||T|}{n}\right| \leq \lambda\sqrt{|S||T|} where e(S,T)e(S,T) counts edges between SS and TT. When λd\lambda \ll d (spectral gap is large), edge distribution is close to random: every pair of large sets has approximately the "expected" number of edges.

ExampleCombinatorial Applications of Spectral Gap

Using the expander mixing lemma for a (n,d,λ)(n, d, \lambda)-graph (regular, nn vertices, degree dd, second eigenvalue λ\leq \lambda):

  • Independence number: α(G)λnd\alpha(G) \leq \frac{\lambda n}{d} (from the Hoffman bound).
  • Chromatic number: χ(G)d/λ\chi(G) \geq d/\lambda (since independent sets are small).
  • Diameter: diam(G)logn/log(d/λ)\mathrm{diam}(G) \leq \lceil\log n / \log(d/\lambda)\rceil (powers of AA fill in edges).
  • Counting subgraphs: the number of copies of small fixed graph HH is close to the expected count in a random dd-regular graph.

Interlacing and Graph Minors

Definition8.7Eigenvalue Interlacing

If BB is a principal submatrix of a symmetric n×nn \times n matrix AA (obtained by deleting rows and columns), then the eigenvalues interlace: λi(A)λi(B)λi+nm(A)\lambda_i(A) \geq \lambda_i(B) \geq \lambda_{i+n-m}(A) where BB is m×mm \times m. For graphs: deleting a vertex can decrease each eigenvalue by at most the amount removed. This constrains how the spectrum changes under vertex deletion and is used to prove bounds on clique number, chromatic number, and maximum cut.

RemarkSpectral Bounds on Max-Cut

For a graph GG with nn vertices and mm edges: MaxCut(G)m/2+(n1)λn(A)/4\mathrm{MaxCut}(G) \geq m/2 + (n-1)\lambda_n(A)/4 where λn\lambda_n is the most negative eigenvalue. For random graphs, λnd\lambda_n \approx -\sqrt{d}, giving MaxCutm/2+cn3/2\mathrm{MaxCut} \approx m/2 + cn^{3/2}. The Goemans-Williamson SDP relaxation achieves a 0.878-approximation for max-cut, with the SDP matrix related to the Laplacian.


Strongly Regular Graphs

Definition8.8Strongly Regular Graphs

A strongly regular graph srg(n,k,λ,μ)\mathrm{srg}(n, k, \lambda, \mu) is kk-regular with every pair of adjacent vertices having λ\lambda common neighbors and every pair of non-adjacent vertices having μ\mu common neighbors. The adjacency matrix satisfies A2=kI+λA+μ(JIA)A^2 = kI + \lambda A + \mu(J - I - A), giving eigenvalues kk (multiplicity 1), r=(λμ)+(λμ)2+4(kμ)2r = \frac{(\lambda-\mu)+\sqrt{(\lambda-\mu)^2+4(k-\mu)}}{2}, s=(λμ)(λμ)2+4(kμ)2s = \frac{(\lambda-\mu)-\sqrt{(\lambda-\mu)^2+4(k-\mu)}}{2}. Feasibility conditions from integrality of multiplicities severely restrict which parameter sets are realizable.

ExampleThe Petersen Graph as srg(10,3,0,1)

The Petersen graph has parameters (10,3,0,1)(10, 3, 0, 1): 3-regular, no triangles (λ=0\lambda = 0), every non-adjacent pair has exactly 1 common neighbor (μ=1\mu = 1). Eigenvalues: 33 (mult. 1), 11 (mult. 5), 2-2 (mult. 4). The three eigenvalues and their multiplicities encode the full structure: the Petersen graph is determined by its spectrum among strongly regular graphs.