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Spectral Graph Theory

12 12. Advanced techniques for random walks on graphs

Definition 400 The spectral parameter \(\lambda \), (12.1) and (12.3)

Let \(G\) be a weighted graph on \(n\) vertices with normalized Laplacian eigenvalues \(0=\lambda _0\le \lambda _1\le \cdots \le \lambda _{n-1}\). For the ordinary random walk we set

\[ \lambda =\begin{cases} \lambda _1 & \text{if }1-\lambda _1\ge \lambda _{n-1}-1,\\ 2-\lambda _{n-1} & \text{otherwise.}\end{cases}\tag {12.1} \]

For the lazy walk of Theorem 1.16 (the “modified walk” defined in (1.16)) one may instead take

\[ \lambda =\begin{cases} \lambda _1 & \text{if }1-\lambda _1\ge \lambda _{n-1}-1,\\[2pt] \dfrac {2\lambda _1}{\lambda _{n-1}+\lambda _1} & \text{otherwise.}\end{cases}\tag {12.3} \]

This \(\lambda \) is the effective spectral gap controlling convergence.

Proposition 401 Eigenvalue convergence bound, (12.2)

Let \(G\) be a weighted graph with transition matrix \(P(u,v)=w_{u,v}/d_u\), stationary distribution \(\pi (x)=d_x/\operatorname {vol}G\) where \(\operatorname {vol}G=\sum _x d_x\), and let \(\lambda \) be as in 400. Then the relative pointwise distance after \(s\) steps satisfies

\[ \Delta (s)=\max _{x,y}\frac{|P^s(y,x)-\pi (x)|}{\pi (x)}\le e^{-c}\qquad \text{whenever}\qquad s\ge \frac{c}{\lambda }\log \frac{\operatorname {vol}G}{\min _x d_x}. \]
Definition 402 Logarithmic Sobolev constant, (12.4)

Let \(G=(V,E)\) be a weighted graph with edge weights \(w_{x,y}\), degrees \(d_x=\sum _y w_{x,y}\) and \(\operatorname {vol}G=\sum _x d_x\). For a nontrivial \(f:V\to \mathbb R\) the relative entropy \(\sum _{x}f^2(x)d_x\log \frac{f^2(x)\operatorname {vol}G}{\sum _z f^2(z)d_z}\) is nonnegative (writing \(\mu (x)=f^2(x)d_x/\sum _z f^2(z)d_z\) it equals \(\big(\sum _z f^2 d_z\big)\, \mathrm{KL}(\mu \Vert \pi )\ge 0\)). The logarithmic Sobolev constant \(\alpha _G=\alpha \) is the largest constant such that the log-Sobolev inequality

\[ \alpha \sum _{x\in V}f^2(x)\, d_x\log \frac{f^2(x)\operatorname {vol}G}{\sum _z f^2(z)d_z}\ \le \ \sum _{\{ x,y\} \in E}(f(x)-f(y))^2 w_{x,y} \]

holds for every nontrivial \(f:V\to \mathbb R\); equivalently

\[ \alpha _G=\alpha =\inf _{f\neq 0}\ \frac{\displaystyle \sum _{\{ x,y\} \in E}(f(x)-f(y))^2 w_{x,y}}{\displaystyle \sum _{x\in V}f^2(x)\, d_x\log \frac{f^2(x)\operatorname {vol}G}{\sum _z f^2(z)d_z}}.\tag {12.4} \]
Definition 403 Log-Sobolev constant with Dirichlet condition, (12.7)

Let \(S\subseteq V(G)\), let \(S^{*}\) be the set of edges with at least one endpoint in \(S\), and let \(\delta S\) be the vertex boundary of \(S\). The Dirichlet log-Sobolev constant is

\[ \alpha _S^{(D)}=\inf _{f}\ \frac{\displaystyle \sum _{\{ x,y\} \in S^{*}}(f(x)-f(y))^2 w_{x,y}}{\displaystyle \sum _{x}f^2(x)d_x\log \frac{f^2(x)\operatorname {vol}S}{\sum _{z\in S}f^2(z)d_z}}\tag {12.7} \]

where \(f\) ranges over all nontrivial \(f:S\cup \delta S\to \mathbb R\) satisfying \(f(y)=0\) for \(y\in \delta S\), and \(\operatorname {vol}S=\sum _{x\in S}d_x\).

Definition 404 Log-Sobolev constant with Neumann condition, (12.8)

With \(S,S^{*},\delta S\) as above, the Neumann log-Sobolev constant is

\[ \alpha _S=\inf _{f\neq c}\ \frac{\displaystyle \sum _{\{ x,y\} \in S^{*}}(f(x)-f(y))^2 w_{x,y}}{\displaystyle \sum _{x}f^2(x)d_x\log \frac{f^2(x)\operatorname {vol}S}{\sum _{z\in S}f^2(z)d_z}}\tag {12.8} \]

where \(f\) ranges over all non-constant \(f:S\cup \delta S\to \mathbb R\). If one restricts to \(f\) normalized by \(\sum _{x\in S}f^2(x)d_x=\operatorname {vol}S\) the denominator simplifies to \(\sum _{x\in S}f^2(x)d_x\log f^2(x)\).

Lemma 405 Lemma 12.1: \(\alpha \le \lambda _1/2\)

For a graph \(G\) (with no boundary, or with Dirichlet or Neumann boundary conditions) the log-Sobolev constant \(\alpha \) and the first Laplacian eigenvalue \(\lambda _1\) satisfy

\[ \alpha \le \frac{\lambda _1}{2}.\tag {12.9} \]
Proof

Let \(f\) be a harmonic eigenfunction associated with \(\lambda _1\), so that its Rayleigh quotient \(\sum _{\{ x,y\} \in E}(f(x)-f(y))^2 w_{x,y}\big/\sum _x f^2(x)d_x=\lambda _1\) and \(\sum _x f(x)d_x=0\). For small \(\epsilon {\gt}0\) put \(f'=1+\epsilon f\). Then, using \(\sum _x f(x)d_x=0\),

\[ \sum _x f'^2(x)d_x=\operatorname {vol}G+\epsilon ^2\sum _x f^2(x)d_x . \]

By 402, \(\alpha \le I/II\) where

\[ I=\sum _{\{ x,y\} \in E}(f'(x)-f'(y))^2 w_{x,y}=\epsilon ^2\sum _{\{ x,y\} \in E}(f(x)-f(y))^2 w_{x,y}, \]

and, using \(\sum _x f(x)d_x=0\) and \(\log (1+x)=x-x^2/2+O(x^3)\),

\[ II=\sum _x f'^2(x)d_x\log \frac{f'^2(x)\operatorname {vol}G}{\sum _z f'^2(z)d_z}=2\epsilon ^2\sum _x f^2(x)d_x+O(\epsilon ^3). \]

Hence \(\alpha \le I/II\to \dfrac {\sum _{\{ x,y\} }(f(x)-f(y))^2 w_{x,y}}{2\sum _x f^2(x)d_x}=\dfrac {\lambda _1}{2}\) as \(\epsilon \to 0\).

Proposition 406 Log-Sobolev constant of a Cartesian product

Just as the eigenvalues of a (weighted) Cartesian product satisfy \(\lambda _{G\otimes H}=\tfrac 12(\lambda _G+\lambda _H)\), the log-Sobolev constants satisfy

\[ \alpha _{G\otimes H}=\tfrac 12(\alpha _G+\alpha _H). \]

In particular for the \(n\)-cube \(Q_n\) (which is an \(n\)-fold product of an edge) one obtains \(\alpha _{Q_n}=\lambda _1/2=1/n\), so the lazy random walk on \(Q_n\) converges in time of order \(n\log n\).

Definition 407 \((\pi ;p)\)-norm, s12.2

Let \(G\) be a weighted graph with stationary distribution \(\pi (x)=d_x/\operatorname {vol}G\), and let \(\Pi \) denote the diagonal matrix with \((x,x)\)-entry \(\pi (x)\). For \(f:V(G)\to \mathbb R\) and \(p\ge 1\) the \((\pi ;p)\)-norm is

\[ {}_\pi \| f\| _p=\Big(\sum _{x\in V(G)}f^p(x)\pi (x)\Big)^{1/p}. \]

In particular \({}_\pi \| f\| _2=\big(\sum _x f^2(x)\pi (x)\big)^{1/2}=\| \Pi ^{1/2}f\| _2\).

Theorem 408 Theorem 12.2: heat-kernel contraction gives pointwise convergence

Suppose the heat kernel \(H_s=e^{-s\mathcal L}\) of a weighted graph \(G\) satisfies

\[ {}_\pi \big\| \, f\, \Pi ^{1/2}H_s\Pi ^{-1/2}\big\| _p\ \le \ {}_\pi \| f\| _2\tag {12.10} \]

for all \(f:V(G)\to \mathbb R\), with \(p=e^{\beta s}\) for some \(\beta {\gt}0\). Then the random walk satisfies \(\Delta (t)\le e^{2-c}\) whenever

\[ t\ge \frac{2}{\beta }\log \log \frac{\operatorname {vol}G}{\min _x d_x}+\frac{c}{\lambda }, \]

where \(\lambda \) is as in 400.

Proof

Define \(q\) by \(\tfrac 1p+\tfrac 1q=1\). For a vertex \(x\) let \(\psi _x\) be the indicator of \(x\). For \(f:V\to \mathbb R\), by Hölder’s inequality

\[ |\psi _x\Pi ^{-1/2}H_s\Pi ^{1/2}f|=\big|(\psi _x\Pi ^{-1+1/q})(\Pi ^{1/p-1/2}H_s\Pi ^{1/2}f)\big|\le {}_\pi \| \psi _x\Pi ^{-1}\| _q\cdot {}_\pi \| f\Pi ^{1/2}H_s\Pi ^{-1/2}\| _p.\tag {12.11} \]

Now

\[ {}_\pi \| \psi _x\Pi ^{-1}\| _q=\big(\pi (x)^{1-q}\big)^{1/q}=\pi (x)^{-1/p}\le \Big(\frac{\operatorname {vol}G}{\min _x d_x}\Big)^{1/p}. \]

Choosing \(s=\tfrac 1\beta \log \log \tfrac {\operatorname {vol}G}{\min _x d_x}\) makes \(p=e^{\beta s}=\log \tfrac {\operatorname {vol}G}{\min _x d_x}\), so \(\big(\tfrac {\operatorname {vol}G}{\min _x d_x}\big)^{1/p}=e\). Combining with 408 (12.10) and (12.11),

\[ |\psi _x\Pi ^{-1/2}H_s\Pi ^{1/2}f|\le e\, {}_\pi \| f\Pi ^{1/2}H_s\Pi ^{-1/2}\| _p\le e\, {}_\pi \| f\| _2 . \]

Let \(I_0\) be the projection onto the trivial eigenfunction \(\phi _0\); since \(H_sI_0=I_0\) we have \(H_{s+r}-I_0=H_s(H_r-I_0)\), and applying the last display to \(g=\Pi ^{-1/2}(H_r-I_0)\Pi ^{1/2}f\) gives

\[ |\psi _x\Pi ^{-1/2}(H_{s+r}-I_0)\Pi ^{1/2}f|\le e\| (H_r-I_0)\Pi ^{1/2}f\| _2\le e\, \| H_r-I_0\| _2\, \| \Pi ^{1/2}f\| _2\le e^{1-\lambda r}\, {}_\pi \| f\| _2, \]

using \(\| H_r-I_0\| _2\le e^{-\lambda r}\). Replacing \(\Pi ^{1/2}f\) by an arbitrary \(g\) yields

\[ \| \psi _x\Pi ^{-1/2}(H_{s+r}-I_0)\| _2\le e^{1-\lambda r}.\tag {12.12} \]

Finally, since \(H_{2s+2r}-I_0=(H_{s+r}-I_0)^2\) is self-adjoint in the \(\pi \)-inner product,

\[ \Delta (2s+2r)\le \max _{x,y}|\psi _x\Pi ^{-1/2}(H_{2s+2r}-I_0)\Pi ^{-1/2}\psi _y|\le \max _{x,y}\| \psi _x\Pi ^{-1/2}(H_{s+r}-I_0)\| _2\, \| \psi _y\Pi ^{-1/2}(H_{s+r}-I_0)\| _2\le e^{2-2\lambda r} \]

by (12.12) and Cauchy–Schwarz. Taking \(r=c/(2\lambda )\) and \(t=2s+2r\) gives \(\Delta (t)\le e^{2-c}\) with \(t=\tfrac 2\beta \log \log \tfrac {\operatorname {vol}G}{\min _x d_x}+\tfrac {c}{\lambda }\).

Theorem 409 Theorem 12.3: heat-kernel contraction gives total-variation convergence

Under the same hypothesis 408 (12.10) with \(p=e^{\beta s}\), the random walk on \(G\) satisfies \(\Delta _{TV}(t)\le \tfrac 12 e^{1-c}\) whenever

\[ t\ge \frac{1}{\beta }\log \log \frac{\operatorname {vol}G}{\min _x d_x}+\frac{c}{\lambda }. \]
Proof

Following the notation of Theorem 12.2, with \(s=\tfrac 1\beta \log \log \tfrac {\operatorname {vol}G}{\min _x d_x}\),

\[ \Delta _{TV}(s+r)=\tfrac 12\max _x\sum _y|\psi _x P^{s+r}(y)-\pi (y)|\le \tfrac 12\max _x\sum _y|\psi _x\Pi ^{-1/2}(H_{s+r}-I_0)\Pi ^{1/2}(y)|\le \tfrac 12 e^{1-\lambda r} \]

by the pointwise bound underlying (12.12). Taking \(r=c/\lambda \) and \(t=s+r\) gives \(\Delta _{TV}(t)\le \tfrac 12 e^{1-c}\) with \(t=\tfrac 1\beta \log \log \tfrac {\operatorname {vol}G}{\min _x d_x}+\tfrac {c}{\lambda }\).

Lemma 410 Elementary power inequality, (12.14)

For all \(a,b\ge 0\) and \(p\ge 1\),

\[ 4(p-1)\big(a^{p/2}-b^{p/2}\big)^2\le p^2(a-b)\big(a^{p-1}-b^{p-1}\big).\tag {12.14} \]
Proof

The case \(p=1\) is trivial (left side \(0\)). For \(p{\gt}1\) assume WLOG \(a\ge b\ge 0\) and write \(a=u^2\), \(b=v^2\) with \(u\ge v\ge 0\); the claim becomes \(4(p-1)(u^p-v^p)^2\le p^2(u^2-v^2)(u^{2p-2}-v^{2p-2})\). Writing each difference as an integral, \(u^p-v^p=\int _v^u p\, t^{p-1}\, dt\), \(u^2-v^2=\int _v^u 2t\, dt\), \(u^{2p-2}-v^{2p-2}=\int _v^u(2p-2)t^{2p-3}\, dt\), we must show

\[ \Big(\int _v^u t^{p-1}\, dt\Big)^2\le \Big(\int _v^u t\, dt\Big)\Big(\int _v^u t^{2p-3}\, dt\Big), \]

after cancelling \(p^2\) and \(4(p-1)\). Since \(t^{p-1}=t^{1/2}\cdot t^{(2p-3)/2}\), this is exactly the Cauchy–Schwarz inequality \(\big(\int t^{1/2}t^{(2p-3)/2}\, dt\big)^2\le \big(\int t\, dt\big)\big(\int t^{2p-3}\, dt\big)\); for \(p{\gt}1\) the integral \(\int _0^u t^{2p-3}\, dt\) converges, covering the boundary case \(b=0\).

In a graph \(G\) with log-Sobolev constant \(\alpha \), the heat kernel \(H_t=e^{-t\mathcal L}\) satisfies

\[ {}_\pi \big\| \, f\, \Pi ^{1/2}H_t\Pi ^{-1/2}\big\| _p\ \le \ {}_\pi \| f\| _2 \]

for every \(t{\gt}0\), with \(p=e^{4\alpha t}+1\), and every \(f:V(G)\to \mathbb R\).

Proof

By 402, for any nontrivial \(f\), \(\sum _{x\sim y}(f(x)-f(y))^2 w(x,y)\ge \alpha \sum _x f^2(x)d_x\log \frac{f^2(x)}{\sum _z f^2(z)\pi (z)}\). Replacing \(f\) by \(f^{p/2}\),

\[ \sum _{x\sim y}\big(f^{p/2}(x)-f^{p/2}(y)\big)^2 w(x,y)\ge \alpha \sum _x f^p(x)d_x\log \frac{f(x)^p}{\sum _z f^p(z)\pi (z)}.\tag {12.13} \]

By Lemma 410 (with \(a=f(x),b=f(y)\)), \(\big(f^{p/2}(x)-f^{p/2}(y)\big)^2\le \frac{p^2}{4(p-1)}(f(x)-f(y))(f^{p-1}(x)-f^{p-1}(y))\), so

\[ \alpha \sum _x f^p(x)\pi (x)\log \frac{f^p(x)}{\sum _z f^p(z)\pi (z)}\le \frac{p^2}{4(p-1)}\sum _{x\sim y}\big(f^{p-1}(x)-f^{p-1}(y)\big)(f(x)-f(y))w_{x,y}.\tag {12.15} \]

Replace \(f\) by \(g=f\Pi ^{1/2}H_t\Pi ^{-1/2}\) and set \(p=p(t)=1+e^{4\alpha t}\), so \(p'=4\alpha (p-1)\). Then (12.15) rearranges to

\[ \frac{p'}{p^2}\sum _x g^p(x)\pi (x)\log \frac{|g(x)|^p}{\sum _z g^p(z)\pi (z)}-\sum _{x\sim y}\big(g^{p-1}(x)-g^{p-1}(y)\big)(g(x)-g(y))w_{x,y}\le 0.\tag {12.16} \]

Define \(F(t)={}_\pi \| g\| _p=G(t)^{1/p}\) with \(G(t)=\sum _x g^p(x)\pi (x)\); then \(F(0)={}_\pi \| f\| _2\). From \(\log F=\tfrac 1p\log G\),

\[ F'(t)=\Big(-\frac{p'}{p^2}\log G(t)+\frac{G'(t)}{pG(t)}\Big)F(t).\tag {12.17} \]

Writing \(G'(t)=I+II\) with \(II=p'\sum _x g^p\pi \log g\) and, using the weighted heat equation \(\frac{d}{dt}H_t=-\mathcal L H_t\) (weighted Lemma 10.3) and \(\mathcal L=\Pi ^{-1/2}(T-A)\Pi ^{... }\) summation by parts,

\[ I=p\, g^{p-1}\Pi ^{1/2}\tfrac {d}{dt}H_t\Pi ^{1/2}f^{*}=-\frac{p}{\operatorname {vol}G}\sum _{x\sim y}\big(g^{p-1}(x)-g^{p-1}(y)\big)(g(x)-g(y))w_{x,y}. \]

Substituting into (12.17) and simplifying,

\[ F'(t)=\frac{1}{\operatorname {vol}G}\Big(\frac{p'}{p^2}\sum _x g^p(x)d_x\log \frac{g^p(x)}{\log G(t)}-\sum _{x\sim y}\big(g^{p-1}(x)-g^{p-1}(y)\big)(g(x)-g(y))w_{x,y}\Big)\le 0 \]

by (12.16). Hence \({}_\pi \| g\| _p=F(t)\le F(0)={}_\pi \| f\| _2\), which is the assertion.

Theorem 412 Theorem 12.5: pointwise convergence from log-Sobolev constant

In a weighted graph \(G\) with log-Sobolev constant \(\alpha \), we have \(\Delta (t)\le e^{2-c}\) whenever

\[ t\ge \frac{1}{2\alpha }\log \log \frac{\operatorname {vol}G}{\min _x d_x}+\frac{c}{\lambda }. \]
Proof

Theorem 411 shows the heat kernel satisfies hypothesis (12.10) of Theorem 408 with \(p=e^{4\alpha t}\) (dropping the additive \(1\)), i.e. with \(\beta =4\alpha \). Substituting \(\beta =4\alpha \) into the bound of Theorem 12.2 gives \(t\ge \frac{2}{4\alpha }\log \log \frac{\operatorname {vol}G}{\min _x d_x}+\frac c\lambda =\frac{1}{2\alpha }\log \log \frac{\operatorname {vol}G}{\min _x d_x}+\frac c\lambda \).

Theorem 413 Theorem 12.6: total-variation convergence from log-Sobolev constant

In a weighted graph \(G\) with log-Sobolev constant \(\alpha \), we have \(\Delta _{TV}(t)\le \tfrac 12 e^{1-c}\) whenever

\[ t\ge \frac{1}{4\alpha }\log \log \frac{\operatorname {vol}G}{\min _x d_x}+\frac{c}{\lambda }. \]
Proof

By Theorem 411 the hypothesis of Theorem 409 holds with \(\beta =4\alpha \). Substituting into the bound of Theorem 12.3 gives \(t\ge \frac{1}{4\alpha }\log \log \frac{\operatorname {vol}G}{\min _x d_x}+\frac c\lambda \).

Lemma 414 Lemma 12.7: comparison theorem for \(\alpha \), (12.18)

Let \(G=(V,E)\) and \(G'=(V',E')\) be connected graphs with log-Sobolev constants \(\alpha =\alpha _G\) and \(\alpha '=\alpha _{G'}\). Suppose \(\rho :V\to V'\) is surjective and:

  • with \(d_x,d'_{x'}\) the degrees in \(V,V'\), for all \(x'\in V'\), \(\displaystyle \sum _{x\in \rho ^{-1}(x')}d_x\ge c\, d'_{x'}\);

  • for each edge \(e=xy\in E\) there is a path \(P(e)\) between \(\rho (x)\) and \(\rho (y)\) in \(E'\) such that

    • \(P(e)\) has at most \(\ell \) edges;

    • for each edge \(e'\in E'\), \(\displaystyle \sum _{e:\, e'\in P(e)}w_e\le m\, w_{e'}\).

Then

\[ \alpha '\ge \frac{c}{\ell m}\, \alpha .\tag {12.18} \]
Proof

Let \(g:V'\to \mathbb R\) achieve \(\alpha '\) and define \(f:V\to \mathbb R\) by \(f(x)=g(\rho (x))\). Writing \(g^2(e')=(g(x')-g(y'))^2\) for \(e'=x'y'\) and \(S(g)=\sum _{x'}g^2(x')d'_{x'}\log \frac{g^2(x')}{\sum _z g^2(z)\pi '(z)}\),

\[ \alpha '=\frac{\sum _{e'\in E'}g^2(e')w_{e'}}{S(g)}=\underbrace{\frac{\sum _{e'}g^2(e')w_{e'}}{\sum _e f^2(e)w_e}}_{I}\cdot \underbrace{\frac{\sum _e f^2(e)w_e}{S(f)}}_{II}\cdot \underbrace{\frac{S(f)}{S(g)}}_{III}.\tag {12.19} \]

Exactly as in the proof of Lemma 4.14, hypotheses (ii)(a),(b) give \(I\ge \frac{1}{\ell m}\), and by definition of \(\alpha \), \(II=\sum _e f^2(e)w_e/S(f)\ge \alpha \). It remains to show \(III\ge c\). Define

\[ F(\xi ,\zeta )=\xi \log \xi -\xi \log \zeta -\xi +\zeta \qquad (\xi ,\zeta {\gt}0), \]

which satisfies \(F(\xi ,\zeta )\ge 0\) and, for fixed \(\zeta \), is convex in \(\xi \). Choosing the common normalizing constant \(c_0\) so that \(S(f)=\sum _{x\in V}F(f^2(x),c_0)d_x\) and \(S(g)=\sum _{x'\in V'}F(g^2(x'),c_0)d'_{x'}\) (possible by convexity, adjusting \(c_0\)), and using \(f^2(x)=g^2(\rho (x))\),

\[ S(f)=\sum _{x'\in V'}\Big(\sum _{x\in \rho ^{-1}(x')}d_x\Big)F(g^2(x'),c_0)\ \ge \ \sum _{x'\in V'}c\, d'_{x'}F(g^2(x'),c_0)=c\, S(g), \]

using \(F\ge 0\) and hypothesis (i). Hence \(III=S(f)/S(g)\ge c\), and \(\alpha '=I\cdot II\cdot III\ge \frac{c}{\ell m}\alpha \).

Corollary 415 Regular case of the comparison theorem

If, in addition to the hypotheses of Lemma 414, \(G\) and \(G'\) are regular with degrees \(k\) and \(k'\) respectively, then

\[ \alpha '\ge \frac{k}{k'\ell m}\, \alpha . \]
Proof

For regular graphs, hypothesis (i) of Lemma 414 holds with \(c=k/k'\) (each fiber \(\rho ^{-1}(x')\) has total degree at least \(\frac{k}{k'}d'_{x'}\)), so substituting \(c=k/k'\) into (12.18) gives \(\alpha '\ge \frac{k}{k'\ell m}\alpha \).

Definition 416 Log-Sobolev constant of a Riemannian manifold, (12.21)
#

For a smooth, compact, connected Riemannian manifold \(M\) with Laplace–Beltrami operator \(\Delta \) and gradient \(\nabla \) (with no boundary, or with convex boundary and Dirichlet or Neumann conditions), the log-Sobolev constant \(\alpha \) is the least value such that

\[ \int _M f^2(x)\log f^2(x)\ \le \ \alpha \int _M|\nabla f(x)|^2 \]

holds for all \(f:M\to \mathbb R\) with \(\int |f|^2=\operatorname {vol}M\).

Lemma 417 Euler–Lagrange equation on a manifold, (12.22)

If \(f\) achieves the log-Sobolev constant \(\alpha \) of \(M\) (as in 416), then

\[ \Delta f=-\alpha f\log f^2.\tag {12.22} \]
Theorem 418 Logarithmic Harnack inequality on manifolds, (12.23)

Let \(f\) satisfy 416 and achieve \(\alpha \), and suppose \(M\) has non-negative Ricci curvature. Then

\[ |\nabla f|^2+\alpha f^2\log f^2\ \le \ \alpha \, \sup _M\big(f^2\log f^2\big).\tag {12.23} \]
Theorem 419 Lower bound for the manifold log-Sobolev constant, (12.24)
#

For a \(d\)-dimensional compact Riemannian manifold \(M\) with non-negative Ricci curvature, diameter \(D(M)\) and first Laplacian eigenvalue \(\lambda _1\),

\[ \alpha \ \ge \ \min \Big\{ \frac{\lambda _1}{8e},\ \frac{1}{d\, D^2(M)}\Big\} .\tag {12.24} \]
Theorem 420 Theorem 12.8: extremal equation of the log-Sobolev function, (12.25)

For a graph \(G\), suppose \(f:V\to \mathbb R\) satisfies \(\sum _x f^2(x)d_x=\operatorname {vol}G\) and achieves the log-Sobolev constant \(\alpha \). Then, with \(L\) the combinatorial Laplacian \(Lf(x)=\sum _{y\sim x}(f(x)-f(y))\), for every vertex \(x\),

\[ Lf(x)=\sum _{y\sim x}(f(x)-f(y))=\alpha \, f(x)\, d_x\log f^2(x).\tag {12.25} \]
Proof

Apply Lagrange’s method, differentiating the log-Rayleigh quotient (the right side of (12.4)) with respect to \(f(x)\). This gives

\[ \frac{2Lf(x)}{\sum _z f^2(z)\log f^2(z)}-\frac{2\big(f(x)d_x\log f^2(x)+2f(x)d_x\big)\sum _{x\sim y}(f(x)-f(y))^2}{\big(\sum _z f^2(z)d_z\log f^2(z)\big)^2}+c_1 f(x)=0\tag {12.28} \]

for some constant \(c_1\). Substituting the value of \(\alpha \) this simplifies to

\[ Lf(x)-\alpha \big(f(x)\log f^2(x)+2f(x)\big)+c_2 f(x)=0.\tag {12.29} \]

Multiplying (12.29) by \(f(x)\) and summing over \(x\in V\),

\[ \sum _{x\sim y}(f(x)-f(y))^2-\alpha \sum _x f^2(x)\big(\log f^2(x)+2\big)+c_2\sum _x f^2(x)=0. \]

Since \(f\) achieves \(\alpha \), \(\sum _{x\sim y}(f(x)-f(y))^2=\alpha \sum _x f^2(x)d_x\log f^2(x)\), so the \(\log f^2\) terms cancel and we are left with \(-2\alpha \sum _x f^2+c_2\sum _x f^2=0\), i.e. \(c_2=2\alpha \). Substituting back into (12.29) gives \(Lf(x)=\alpha f(x)d_x\log f^2(x)\).

Theorem 421 Theorem 12.9: logarithmic Harnack inequality, invariant homogeneous graph, (12.26)

In an invariant homogeneous graph \(\Gamma \) with edge generating set \(\mathcal K\) of \(k\) generators, suppose \(f:V(\Gamma )\to \mathbb R\) achieves the log-Sobolev constant and satisfies \(\sum _x f^2(x)d_x=\operatorname {vol}G\). Then, with \(U=\sup _y|f(y)|\ge 1\), for every \(x\in V(\Gamma )\),

\[ \sum _{a\in \mathcal K}\big(f(x)-f(ax)\big)^2\ \le \ 6k\alpha \, \max \Big\{ U^2\log U^2\big(1+\tfrac \alpha 4\log ^2 U^2\big),\, 1\Big\} .\tag {12.26} \]
Theorem 422 Theorem 12.10: logarithmic Harnack inequality, abelian homogeneous subgraph

In an abelian homogeneous graph \(\Gamma \) with edge generating set \(\mathcal K\) of \(k\) generators, let \(S\) be a strongly convex subgraph and suppose \(f:S\cup \delta S\to \mathbb R\) satisfies the Dirichlet or Neumann boundary condition, achieves the log-Sobolev constant, and \(\sum _{x\in S}f^2(x)d_x=\operatorname {vol}S\). Then, with \(U=\sup _y|f(y)|\), for every \(x\in S\),

\[ \sum _{a\in \mathcal K}\big(f(x)-f(ax)\big)^2\ \le \ 6k\alpha \, \max \Big\{ U^2\log U^2\big(1+\tfrac \alpha 4\log ^2 U^2\big),\, 1\Big\} . \]
Theorem 423 Theorem 12.11: sup bound for the extremal function

In a connected invariant homogeneous graph \(\Gamma \) with edge generating set \(\mathcal K\) of \(k\) generators, if \(f:V(\Gamma )\to \mathbb R\) achieves the log-Sobolev constant and \(\sum _x f^2(x)d_x=\operatorname {vol}G\), then

\[ U=\sup _y|f(y)|\ \le \ e^{k}, \]

where \(e\) is the base of the natural logarithm.

Theorem 424 Theorem 12.12: log-Sobolev lower bound, invariant homogeneous graph

In a connected invariant homogeneous graph \(G=(V,E)\), suppose \(f:V\to \mathbb R\) satisfies the logarithmic Harnack inequality and \(\sum _x f^2(x)d_x=\operatorname {vol}G\). Then the log-Sobolev constant \(\alpha \) satisfies

\[ \alpha \ \ge \ \min \Big\{ \frac{1}{32kD^2},\ \frac{1}{24kD^2\log U^2}\Big\} , \]

where \(U=\sup _z|f(z)|\), \(k\) is the degree and \(D\) is the diameter of \(G\).

Theorem 425 Theorem 12.13: log-Sobolev lower bound, abelian homogeneous subgraph

Let \(S\) be a strongly convex subgraph of a connected abelian homogeneous graph \(G\), and suppose \(f:V\to \mathbb R\) satisfies the Dirichlet or Neumann boundary condition, achieves the log-Sobolev constant \(\alpha \), and \(\sum _{x\in S}f^2(x)d_x=\operatorname {vol}S\). Then

\[ \alpha \ \ge \ \min \Big\{ \frac{\lambda _1}{4},\ \frac{1}{24kD^2\log U^2}\Big\} , \]

where \(U=\sup _z|f(z)|\), \(k\) is the degree, \(D\) is the diameter of \(S\), and \(\lambda _1\) is the first eigenvalue of the Laplacian of \(G\).

Lemma 426 Eigenvalue bound for homogeneous graphs

A \(k\)-regular abelian homogeneous graph, or a strongly convex subgraph of one, satisfies the eigenvalue bound

\[ \lambda _1\ \ge \ \frac{1}{8kD^2}, \]

where \(D\) is the diameter, and this lower bound is sharp up to the constant factor of \(k\) (the factor \(k\) is necessary for some homogeneous graphs).

Theorem 427 Theorem 12.14: log-Sobolev lower bound via isoperimetric dimension, (12.27)

Let \(S\) be a strongly convex subgraph of a connected homogeneous invariant graph with isoperimetric dimension \(\delta \), and suppose \(f:V\to \mathbb R\) satisfies the Dirichlet or Neumann boundary condition, achieves the log-Sobolev constant, and \(\sum _{x\in S}f^2(x)d_x=\operatorname {vol}S\). Then

\[ \alpha \ \ge \ \min \Big\{ \frac{\lambda _1}{4},\ \frac{1}{24kD^2\delta \log \delta }\Big\} ,\tag {12.27} \]

where \(U=\sup _z|f(z)|\), \(k\) is the degree, \(D\) is the diameter of \(S\), and \(\lambda _1\) is the first eigenvalue of the Laplacian of \(G\). Consequently a random walk on such a graph with bounded isoperimetric dimension converges in time of order \((\log \log n)\, kD^2\).

Theorem 428 Theorem 12.15: discrete De Giorgi–Nash–Moser regularity

Let \(G\) be a weighted graph with isoperimetric dimension \(\delta {\gt}2\), i.e. for every \(X\subseteq V(G)\) with \(\operatorname {vol}X\le \operatorname {vol}\bar X\) one has \(\sum _{x\in X,\, y\notin X}w_{x,y}\ge c_\delta (\operatorname {vol}X)^{(\delta -1)/\delta }\). If \(f\) is a harmonic eigenfunction with eigenvalue \(\lambda \), then

\[ \sup _x|f^2(x)|\ \le \ c^{\delta /2}\, \lambda ^{\delta /2}\sum _x f^2(x)d_x \]

for some absolute constant \(c\).

Proof

Since \(f\) is a harmonic eigenfunction, \(Lf(x)=\lambda d_x f(x)\) with \(L\) the combinatorial Laplacian; WLOG \(f\ge 0\). By summation by parts and Lemma 410,

\[ \sum _x\lambda f^p(x)d_x=\sum _x f^{p-1}(x)Lf(x)=\sum _{x\sim y}(f(x)-f(y))(f^{p-1}(x)-f^{p-1}(y))w(x,y)\ge \sum _{x\sim y}\frac{4(p-1)}{p^2}\big(f^{p/2}(x)-f^{p/2}(y)\big)^2 w(x,y).\tag {12.31} \]

Apply the discrete Sobolev inequality (12.30), \(\sum _{u\sim v}|g(u)-g(v)|^2 w(x,y)\ge c\big(\sum _v|g(v)|^{2\gamma }d_v\big)^{1/\gamma }\) with \(\gamma =\frac{\delta }{\delta -2}\) and \(c=c_\delta (\delta -1)^2/(4\delta ^2)\), taking \(g=f^{p/2}\):

\[ \Big(\sum _x f^{\gamma p}(x)d_x\Big)^{1/\gamma }\le c^{-1}\sum _{x,y}\big(f^{p/2}(x)-f^{p/2}(y)\big)^2 w(x,y)\le c^{-1}\frac{p^2}{4(p-1)}\lambda \sum _x f^p(x)d_x, \]

i.e., for \(q\ge 0\),

\[ \Big(\sum _x f^{\gamma q}(x)d_x\Big)^{1/\gamma }\le c^{-1}\frac{q^2}{q-1}\lambda \sum _x f^q(x)d_x.\tag {12.32} \]

Iterating from \(q=2\) (where \(\big(\sum f^{2\gamma }d\big)^{1/\gamma }\le c_1\, 4\lambda \sum f^2 d\), \(c_1=c^{-1}\)) through \(q=2\gamma ,2\gamma ^2,\dots ,2\gamma ^{i}\):

\[ \Big(\sum _x f^{2\gamma ^i}(x)d_x\Big)^{1/\gamma ^i}\le (c_1\lambda )^{1+\frac1\gamma +\cdots +\frac1{\gamma ^{i-1}}}\, 4\Big(\frac{4\gamma ^2}{2\gamma -1}\Big)^{1/\gamma }\Big(\frac{4\gamma ^4}{2\gamma ^2-1}\Big)^{1/\gamma ^2}\cdots \sum _x f^2(x)d_x. \]

As \(i\to \infty \) the left side tends to \(\sup _x f^2(x)\). Moreover \(1+\frac1\gamma +\cdots \le \frac{1}{1-1/\gamma }=\frac\delta 2\), and \(4\big(\frac{4\gamma ^2}{2\gamma -1}\big)^{1/\gamma }\big(\frac{4\gamma ^4}{2\gamma ^2-1}\big)^{1/\gamma ^2}\cdots \le 4^{\delta /2}\gamma ^{\frac1\gamma +\frac2{\gamma ^2}+\cdots }\le 4^{\delta /2}\gamma ^{\delta /(2\gamma -2)}\le (4e^2)^{\delta /2}\). Hence

\[ \sup _x|f^2(x)|\le c_2^{\delta /2}\lambda ^{\delta /2}\sum _x f^2(x)d_x,\qquad c_2=4e^2 c_1=4e^2/c. \qedhere \]
Corollary 429 Corollary 12.16: general form of the Moser bound

For a weighted graph \(G\) with isoperimetric dimension \(\delta \), suppose \(f\) satisfies \(\sum _x\lambda f^p(x)d_x\ge \sum _x f^{p-1}(x)Lf(x)\) (for the relevant range of \(p\)). Then

\[ \sup _x|f^2(x)|\ \le \ c^{\delta /2}\lambda ^{\delta /2}\sum _x f^2(x)d_x \]

for some absolute constant \(c\).

Proof

The proof of Theorem 428 used the eigenfunction property only through inequality (12.31), i.e. \(\sum _x\lambda f^p d_x\ge \sum _{x\sim y}\frac{4(p-1)}{p^2}(f^{p/2}(x)-f^{p/2}(y))^2 w\). The stated hypothesis \(\sum _x\lambda f^p d_x\ge \sum _x f^{p-1}Lf\) yields exactly (12.31), and the Moser iteration then gives the same conclusion.

In a weighted graph \(G\) with isoperimetric dimension \(\delta \) and isoperimetric constant \(c_\delta \), the random walk approaches stationarity in

\[ O\! \Big(\frac{\log \operatorname {vol}G}{\lambda \delta }+\frac{c'}{\lambda }\Big)\ \text{steps},\qquad c'=\log (c''c_\delta ^{-1}), \]

for some absolute constant \(c''\), where \(\lambda =\lambda _1\) if \(1-\lambda _1\ge \lambda _{n-1}-1\) and \(\lambda =2\lambda _1/(\lambda _1+\lambda _{n-1})\) otherwise. More precisely \(\Delta (t)\le e^{-c}\) once \(t\ge \frac{c\log \operatorname {vol}G}{\lambda \delta }+\frac{c'}{\lambda }\).

Proof

Replace \(f\) by \(g_x=\psi _x\Pi ^{1/2}(H_t-I_0)\Pi ^{-1/2}\) in Theorem 428 (via Corollary 429). Using self-adjointness of \(H_{2t}-I_0\),

\[ \sum _y g_x^2(y)d_y=\operatorname {vol}G\; \psi _x\Pi ^{-1/2}(H_{2t}-I_0)\Pi ^{-1/2}\psi _x\le e^{-2t\lambda }\| \Pi ^{-1/2}\psi _x\| ^2\operatorname {vol}G\le e^{-2t\lambda }\frac{\operatorname {vol}G}{d_x}. \]

Since \(g_x\perp \phi _0\), we have \(e^{-t\lambda }\sum _y g_x^p(y)d_y\ge \sum _y g_x^{p-1}(y)Lg_x(y)\), so Corollary 429 gives

\[ \sup _z|g_x^2(z)|\le c^{\delta /2}e^{-\lambda t\delta /2}\sum _y g_x^2(y)d_y. \]

Therefore

\[ \Delta (t)=\max _{x,y}\frac{|P^t(y,x)-\pi (x)|}{\pi (x)}=\max _x\max _y|g_x(y)|\le \max _x c^{\delta /2}e^{-t\lambda \delta /2}\sum _y g_x^2(y)d_y\le c^{\delta /2}e^{-t\lambda \delta /2}\frac{\operatorname {vol}G}{\min _x d_x}, \]

with \(c=c''c_\delta ^{-1}\). Requiring the right side \(\le e^{-c_0}\) gives \(t\ge \frac{2c_0+\delta \log c+2\log (\operatorname {vol}G/\min _x d_x)}{\lambda \delta }=O\big(\frac{\log \operatorname {vol}G}{\lambda \delta }+\frac{c'}{\lambda }\big)\) with \(c'=\log (c''c_\delta ^{-1})\).

Lemma 431 Harper’s edge-isoperimetric inequality for the hypercube

In the \(n\)-cube \(Q_n\), among all vertex sets of a given cardinality the initial segments of the Hamming (binary) order minimize the edge boundary. Consequently the edge boundary of \(X\subseteq V(Q_n)\) with \(\operatorname {vol}X=m\) is minimized (up to lower-order terms) when \(X\) is close to a Hamming ball, which yields the isoperimetric estimates for \(Q_n\) used below.

Proposition 432 Random walk on the cube and the punctured cube

For a fixed volume \(m\), the edge boundary of \(X\subseteq V(Q_n)\) with \(\operatorname {vol}X=m\) is minimized when \(X\) is close to a Hamming ball; hence the \(n\)-cube \(Q_n\) has isoperimetric dimension \(\delta =n/\log n\) and isoperimetric constant \(c_\delta =1\). By Theorem 430,

\[ \Delta (t)\le \frac{\log \operatorname {vol}Q_n}{\delta \lambda }\le n\log n. \]

For a punctured cube \(Q_n'\) (obtained by removing one vertex from \(Q_n\)) the isoperimetric dimension is virtually unchanged, so its (lazy) random walk also converges in time \(O(n\log n)\).