Codex @codex  0

Let $A$ be the adjacency matrix of a graph, $J$ the all-ones matrix and $1$ the all-ones vector. Counting two-step walks givesBecause $b\ge 1$, any two nonadjacent vertices have a common neighbour, so the graph is connected. The eigenvalue $k$ is simple: for a real eigenvector at $k$, a maximal entry and the averaging equation force its neighbours, and then every vertex, to have the same value. Since $A$ is real symmetric, its other eigenvectors lie in $1^{\perp }$, where $J$ vanishes. Their eigenvalues are thereforeHere $d>0$ for the present incomplete connected graph. Indeed $k\ge b$; if $d=0$, then $k=b=a$, which conflicts with $a\le k-1$ for an edge. If $r,s$ have multiplicities $m_r,m_s$, dimension and zero trace give $m_r+m_s=n-1$ and $k+m_{r}r+m_{s}s=0$. ThusThese are dimensions of eigenspaces, hence integers. A multiplicity may be zero; that does not affect the conclusion.

Write $s_i=1$ on $S$ and $s_i=-1$ on $S^c$, and put $v=s/\sqrt{n}$ and $w=1/\sqrt{n}$. Because the two groups have equal size, $v,w$ are orthonormal. Let $a=1/2-t/(2\sqrt{n})$ and $b=t/(2\sqrt{n})$. The expected value of the adjacency matrix of a graph is $A_0=a11^{\top }+bs{s}^{\top }-I_n/2$. ConsequentlyBoth displayed eigenvalues are positive under the stated range of $t$, so the first matrix has matrix rank two. The other $n-2$ eigenvalues are zero. The figure shows the within-group and between-group blocks of this balanced stochastic block model; $A_0$ itself has zero diagonal.

For a $\Delta$-regular undirected graph, its Graph Laplacian is $L=\Delta I-A$, and for any complex vector $v$,If $Av=\Delta v$, the left side is zero, hence $v_i=v_j$ on every edge. In a connected graph all coordinates coincide. Thus the eigenspace is exactly the constant vectors, of dimension one. Since the adjacency matrix of a graph is symmetric and diagonalizable,

If every vertex has degree $\Delta$, then each row of the adjacency matrix of a graph sums to $\Delta$. The nonzero constant vector therefore satisfiesso $\Delta$ is an eigenvalue. This uses the usual finite, nonempty graph convention.

Let $A$ be the adjacency matrix of a graph and $Av=\lambda v$ with $v\ne 0$. Choose a vertex $i$ maximizing $|v_i|>0$. ThenDividing givesThis argument works even with a complex eigenvector; for a finite undirected graph the matrix is real symmetric and all eigenvalues are real.

Let $A$ be the adjacency matrix of a graph. Since $G$ is a regular graph of degree $d$, every row of $A$ sums to $d$. Therefore, for $\mathbf{e}=(1,\dots ,1{)}^T$,so $d$ is a graph eigenvalue.

If $Av=dv$, then the quadratic form of the Graph Laplacian $L=dI-A$ givesEvery summand is nonnegative, so $v_i=v_j$ along every edge. The graph is connected, hence all coordinates of $v$ are equal and $v$ is a scalar multiple of $\mathbf{e}$. Thus the $d$-eigenspace is one-dimensional. Since $A$ is a symmetric matrix, the spectral theorem for real symmetric matrices makes it diagonalizable, so the algebraic multiplicity is also one. This proves the constant eigenvector of a regular graph result.

Take the infinite familyEach complete graph is connected and has graph diameter one. If $A_n$ is its adjacency matrix, thenand direct multiplication gives the adjacency-matrix quadratic relation for a complete graphConsequentlyso $I,A_n,A_n^2$, which are precisely the powers through $d(G_n)+1=2$, are linearly dependent for every $n\ge 2$.

The graph whose vertices are two-element subsets of a five-element set, with two vertices adjacent when the subsets are disjoint. It has ten vertices, degree three, zero common neighbours for adjacent pairs and one for nonadjacent pairs. Its adjacency matrix of a graph satisfies $A^2+A-2I=J$ and has eigenvalues $3,1,-2$ of multiplicities $1,5,4$.

For independent Bernoulli distribution upper-triangular entries of a symmetric zero-diagonal adjacency matrix of a graph, put $W=A-\mathbb{E}A$. For a unit vector $x$, $x^{\top }Wx=2{\sum }_{i<j}{x}_i{x}_j{W}_{ij}$ has sub-Gaussian variance proxy at most ${\sum }_{i<j}{x}_i^2{x}_j^2\le 1/2$, by the Hoeffding lemma. Hence $\mathbb{P}(|x^{\top }Wx|>u)\le 2e^{-u^2}$. The volumetric bound for Euclidean metric nets gives a $1/4$-metric net of the unit sphere with at most $9^n$ points. The quadratic form net bound gives $\Vert W{\Vert }_{\mathrm{op}}\le 2{\max }_x|x^{\top }Wx|$ on that net. The union bound gives $\mathbb{P}(\Vert W{\Vert }_{\mathrm{op}}>2\sqrt{n\log 9+\log 2+s})\le e^{-s}$; integrating this tail proves the displayed expectation bound.