How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Adjacency spectrum, spectral radius, and cospectral graphs
Definition
Let be a finite simple graph, put , and let be an adjacency matrix of (The adjacency matrix of a finite simple graph).
Because is real symmetric, the real spectral theorem gives a basis of real eigenvectors and shows that all roots of its characteristic polynomial are real (Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis, For , the characteristic polynomial is when , with for the unique matrix, For every finite-dimensional space, is exactly the set of roots in of ). If , we therefore list the eigenvalues in weakly decreasing order
If , this list is empty. In either case, the multiset , counted with multiplicities, is the adjacency spectrum of .
The adjacency spectral radius of is when , and otherwise is
Two finite graphs are cospectral when their adjacency spectra agree as multisets.
Depends on
- The adjacency matrix of a finite simple graph
- For $A\in M_n(F)$, the characteristic polynomial is $\chi_A(x)=\det(xI_n-A)$ when $n\geq1$, with $\chi_A(x)=1$ for the unique $0\times0$ matrix
- Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis
- For every finite-dimensional space, $\sigma_F(T)$ is exactly the set of roots in $F$ of $\chi_T$
Used by
- The matrix-tree theorem becomes an eigenvalue product formula Corollary
- Two cospectral graphs need not be isomorphic Counterexample
- An (n,d,λ)-graph and an expander Definition
- The adjacency spectrum is an isomorphism invariant Proposition
- A finite simple graph is bipartite if and only if its adjacency spectrum is symmetric about 0 Theorem
- The adjacency spectral radius lies between the average degree and the maximum degree Theorem
- The complete bipartite graph K_m,n has adjacency spectrum {√mn,0ᵐ⁺ⁿ⁻²,-√mn} Theorem
- The complete graph Kₙ has adjacency spectrum {n-1,(-1)ⁿ⁻¹} Theorem
- The cycle graph Cₙ has adjacency spectrum {2 cos(2π j/n):0≤ j<n} Theorem
- The Petersen graph has adjacency spectrum {3,1⁵,(-2)⁴} Theorem
Dependency tree · two levels
17 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Richard P. Stanley, Enumerative Combinatorics, Volume 1, Section 4.7 (standard reference, not scraped)