Math 202C: Lecture 5

We continue with our study of random walks on finite graphs via linear algebra. Let \mathrm{G} be a graph on \{1,\dots,n\}, possibly with loops and/or multiple edges. As in Lecture 4, let P^r(i,j) denote the probability that simple random walk on \mathrm{G} starting from vertex i is located at vertex j at time r. In this Lecture, we will use the linear algebra setup from Lecture 4 to analyze the r \to \infty asymptotic behavior of the probability P^r(i,j), which corresponds to the “long time behavior” of simple random walk on \mathrm{G}.

The linear algebraic setup is as follows. Let \mathcal{H} be a Hilbert space with orthonormal basis \{\mathbf{e}_1,\dots,\mathbf{e}_n\} indexed by the vertices of \mathrm{G}. In the language of quantum mechanics, by replacing the vertex set of \mathrm{G} with the Hilbert space \mathcal{H} we allow ourselves to consider not just vertices, but “superpositions” of vertices. Recall from Lecture 4 that the transition operator T \in \mathrm{End}\mathcal{H} for simple random walk on \mathrm{G} is defined by its action on the vertex basis as

T\mathbf{e}_i = \sum\limits_{j=1}^n \frac{m_{ij}}{d_i} \mathbf{e}_j,

where m_{ij} is the number of edges of \mathrm{G} incident to both i and j, and d_i=\sum_{j=1}^n m_{ij} is the total number of edges incident to i. In Lecture 4, we showed that

P^r(i,j) = \langle T^r\mathbf{e}_i,\mathbf{e}_j \rangle,

so that understanding the r \to \infty asymptotics of the probability P^r(i,j) is equivalent to understanding the r \to \infty asymptotics of the scalar product \langle T^r\mathbf{e}_i,\mathbf{e}_j \rangle. We would like to accomplish this via spectral analysis of T.

We adopt the same assumptions as in Lecture 4: \mathrm{G} is a connected graph on \{1,\dots,n\} and n \geq 2, so that in particular every vertex has strictly positive degree. Under this assumption, we showed previously that, although T need not be selfadjoint, or even normal, the “symmetrize” transition operator D^{-1}TD is selfadjoint, with D \in \mathrm{GL}(\mathcal{H}) being the operator that acts diagonally in the vertex basis according to

D\mathbf{e}_j = \sqrt{d_j}\mathbf{e}_j.

We may thus invoke the spectral theorem to assert that

D^{-1}TD = \lambda_1P_1 + \dots + \lambda_mP_m,


\lambda_1 >\dots > \lambda_m,

are the distinct eigenvalues of the selfadjoint operator D^{-1}TD, with P_i \in \mathrm{End}\mathcal{H} the orthogonal projection of \mathcal{H} onto the \lambda_i-eigenspace. Because the P_i‘s are orthogonal projections, we have

D^{-1}T^rD = \lambda_1^rP_1 + \dots + \lambda_m^rP_m,

so that for the rth power of the transition operator we have the decomposition

T^r = \lambda_1^r DP_1D^{-1} + \dots + \lambda_m^r DP_mD^{-1}.

So far everything we have said is valid for an arbitrary operator in \mathrm{End}\mathcal{H} whose orbit under the conjugation action of \mathrm{GL}(\mathcal{H}) contains a selfadjoint operator. To go further we naturally have to reference some of the particular features of the transition operator T.

Theorem 1: The largest eigenvalue of T is \lambda_1=1.

Proof: Let \|\cdot\|_1 be the 1-norm on \mathcal{H} corresponding to the basis \mathbf{e}_1,\dots,\mathbf{e}_n, i.e.

\|\mathbf{v}\|_1 = \sum\limits_{j=1}^n |\langle \mathbf{v},\mathbf{e}_j \rangle|.

We claim that T has norm at most 1 in the corresponding operator norm on \mathrm{End}\mathcal{H}, i.e.

\|T\mathbf{v}\|_1 \leq \|\mathbf{v}\|_1 \quad \forall \mathbf{v} \in \mathcal{H}.

Indeed, we have

\|T\mathbf{v}\|_1 = \sum\limits_{j=1}^n |\langle S\mathbf{v},\mathbf{e}_j\rangle| = \sum\limits_{j=1}^n \left| \left\langle \sum\limits_{i=1}^n \langle \mathbf{e}_i,\mathbf{v}\rangle T\mathbf{e}_i,\mathbf{e}_j \right\rangle \right| \leq \sum\limits_{i=1}^n|\langle \mathbf{v},\mathbf{e}_i\rangle|\sum\limits_{j=1}^n |\langle T\mathbf{e}_i,\mathbf{e}_j\rangle|,

and the terminal double sum is exactly \|\mathbf{v}\|_1, since

\sum\limits_{j=1}^n \langle T\mathbf{e}_i,\mathbf{e}_j \rangle = \sum\limits_{j=1}^n \frac{m_{ij}}{d_i} = \frac{d_i}{d_i}=1.

It follows that any eigenvalue of T has modulus at most 1.

Now we exhibit an eigenvector of T with eigenvalue 1. Consider the vector

\mathbf{s} = \sum\limits_{i=1}^n \frac{d_i}{D}\mathbf{e}_i,


D=\sum\limits_{i=1}^n d_i

is the sum of the degrees of all vertices of \mathrm{G}, which is equal to twice the number of edges in \mathrm{G} less the number of loops. We then have

T\mathbf{s} = \sum\limits_{i=1}^n \frac{d_i}{D}T\mathbf{e}_i = \sum\limits_{i=1}^n \frac{d_i}{D}\sum\limits_{j=1}^n \frac{m_{ij}}{d_i} \mathbf{e}_j = \sum\limits_{j=1}^n \frac{d_j}{D}\mathbf{e}_j = \mathbf{s}.

— Q.E.D.

The eigenvector \mathbf{s} above is called the stationary vector for simple random walk on \mathrm{G}, and it encodes the probability measure on the vertices of \mathrm{G} which assigns mass \frac{d_i}{D} to vertex i. Note that, if \mathrm{G} is a simple graph with constant vertex degree equal to d, which is always the case for Cayley graphs corresponding to a symmetric generating set, then the stationary vector

\mathbf{s} = \sum\limits_{i=1}^n \frac{d}{dn}\mathbf{e}_i = \sum\limits_{i=1}^n \frac{1}{n}\mathbf{e}_i

corresponds to uniform probability measure on \{1,\dots,n\}.

It is important to note that Theorem 1 does not preclude the possibility that the smallest eigenvalue of the transition operator T could also have maximum modulus — it could well be that \lambda_m=-1. However, this can only be the case if \mathrm{G} is bipartite (perhaps I should fill in a proof of this later, but if I don’t you can find one in any text on spectral graph theory). Therefore, we have the following.

Corollary 1: If \mathrm{G} is not bipartite, then

\lim\limits_{r \to \infty} \|T^r-DP_1D^{-1}\| = 0,

where \|\cdot\| is any norm on \mathrm{End}\mathcal{H}.

Proof: Let \|\cdot\| be an arbitrary norm on \mathrm{End}\mathcal{H} (indeed, since \mathrm{End}\mathcal{H}\} is finite-dimensional, all norms are equivalent). Then for any r we have that

\|T^r-\lambda_1DP_1D^{-1}\| \leq \sum\limits_{i=2}^m |\lambda_i|^r \|DP_iD^{-1}\|,

by the triangle inequality. But since \lambda_1=1 and |\lambda_i|<1 for i > 1, the statement follows from the squeeze theorem.

— Q.E.D.

At this point we can finally say something about the probability P^r(i,j) = \langle T^r\mathbf{e}_i,\mathbf{e}_j \rangle that simple random walk on \mathrm{G} starting at i arrives at j at time r: for \mathrm{G} a connected non-bipartite graph on n \geq 2 vertices, the limit

t(i,j) = \lim\limits_{r \to \infty} P^r(i,j)

exists. Note that the role of bipartiteness here is quite clear from a combinatorial perspective: if \mathrm{G} is bipartite, then with probability one simple random walk started at i is located in the color class of i at even times, and in the opposite color class at odd times, so that the above limit obviously does not exist.

Continuing on under the assumption that \mathrm{G} is not-bipartite, let us define a vector \mathbf{t}_i \in \mathcal{H} by

\mathbf{t}_i = \sum\limits_{j=1}^n t(i,j) \mathbf{e}_j,

which encodes the limit distribution of simple random walk on \mathrm{G} started at i.

Theorem 2: For any 1 \leq i \leq n, the limit vector \mathbf{t}_i is an eigenvector of T with eigenvalue 1.

Proof: We have

T\mathbf{t}_i = T\lim\limits_{r \to \infty} T^r \mathbf{e}_i = \lim\limits_{r \to \infty} T^{r+1} \mathbf{e}_i = \mathbf{t}_i.

— Q.E.D.

Theorem (Markov limit theorem): For \mathrm{G} a connected non-bipartite graph on \{1,\dots,n\}, n \geq 2, we have

\mathbf{t}_1=\mathbf{t}_2=\dots = \mathbf{t}_n = \mathbf{s}.

Proof: The statement follows if we can show that \lambda_1=1 is a simple eigenvalue of T, i.e. that the \lambda_1-eigenspace is a line. Indeed, all the vectors \mathbf{t}_i as well as \mathbf{s} have nonnegative coordinates summing to 1 relative to the vertex basis, so if any of these is a scalar multiple of another then the scalar in question must be 1.

The proof that the top eigenspace is indeed one-dimensional uses a bit of a deus ex machina, namely the Perron-Frobenius theorem. Look up this theorem, and complete the proof!

— Q.E.D.

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