random walk on graphs and its algorithmic applications shengyu zhang winter school, itcsc@cuhk, 2009

Random Walk on Graphs and its Algorithmic ApplicationsShengyu Zhang

Winter School, ITCSC@CUHK, 2009

Random walk on graphs

On an undirected graph G: Starting from vertex v0

Repeat for a number of steps: Go to a random neighbor.

Simple but powerful.

Road map: Random walk

Parameters/Properties

Hitting time

Algorithms

k-SAT st-connectivity

Mixing time PageRank Approximate counting Error-reduction

Road map: Quantum walk

Types

Discrete QW

Algorithms

Element Distinctness

Continuous QW Formula Evaluation

Short intro to math model of quantum mechanics

PART I. Random Walk

Key parameter 1: Hitting time

Hitting time

Recall the process of random walk on a graph G. Starting vertex v0

Repeat for a number of steps: Go to a random neighbor.

Hitting time: H(i,j) = expected time to visit j (for the first time), starting at i

i

j

Undirected graphs

Complete graph H(i,j) = n-1 (i≠j)

Line: H(i,j) = j2-i2 (i<j) In particular, H(0,n-1) = (n-1)2.

General graph: H(i,j) = O(n3).

0 1 2 n-1…

(n/2)-complete graph

(n/2)-line

i j

Algorithm 1: 2-SAT

k-SAT: satisfiability of k-CNF formula n variables x1, …, xn∊{0,1} m clauses, each being OR of k literals

Literal: xi or ¬xi

e.g. (k=3): (¬x1) ٧ x5 ٧ x7

3-CNF formula: AND of these m clauses e.g. ((¬x1) ٧ x5 ٧ x7) ٨ (x2 ٧ (¬x5) ٧ (¬x7)) ٨ (x1 ٧ x7 ٧ x8)

3-SAT Problem: Given a 3CNF formula, decide whether there is an assignment of variables s.t. the formula evaluates to 1. For the above example, Yes: x5=1, x7=0, x1=1

P vs. NP

P: problems that can be easily solved “easily”: in polynomial time

NP: problems that can be easily verified. Formally: ∃ a polynomial time verifier V, s.t. for any input

x, If the answer is YES, then ∃y s.t. V(x,y) = 1 If the answer is NO, then ∀y, V(x,y) = 1

The question of TCS: Is P = NP? Intuitively, no. NP should be much larger.

It’s much easier to verify (with help) than to solve (by yourself) mathematical proof, appreciation of good music/food, …

Formal proof? We don’t know yet. One of the 7 Millennium Problems by CMI.①

① http://www.claymath.org/millennium/P_vs_NP/

NP-completeness

k-SAT is NP-complete, for any k ≥ 3. NP-complete:

In NP All other problems in NP can be reduced to it in poly. time

NP-complete problems are the hardest ones in NP. 3-SAT is in NP:

witness --- satisfying assignment A Verification: evaluate formula with variables assigned by A

[Cook-Levin] 3-SAT is NP-complete.

How about 2-SAT?

While 3-SAT is the hardest in NP, 2-SAT is solvable in polynomial time.

Here we present a very simple randomized algorithm, which has polynomial expected running time.

Algorithm for 2-SAT

2SAT: each clause has two variables/negations

Alg [Papadimitriou]: Pick any assignment Repeat O(n2) time

If all satisfied, done Else

Pick any unsatisfied clause Pick one of the two literals each with ½ probability, and flip the

assignment on that variable

(x1 x∨ 2) (x∧ 2 ¬x∨ 3) (¬x∧ 4 x∨ 3) (x∧ 5 x∨ 1)

x1, x2, x3, x4, x5

0, 1, 0, 1, 01

Analysis

(x1 x∨ 2) (x∧ 2 ¬x∨ 3) (¬x∧ 4 x∨ 3) (x∧ 5 x∨ 1) x1, x2, x3, x4, x5 0, 1, 0, 1, 0

If unsatisfiable: never find an satisfying assignment If satisfiable, there exists a satisfying assignment x

If our initially picked assignment x’ is satisfying, then done. Otherwise, for any unsatisfied clause, at least one of the

two variables is assigned a value different than that in x Randomly picking one of the two variables and flipping its

value increases # correct assignments by 1 w.p. ≥ ½

Analysis (continued)

Consider a line of n+1 points, where k represents “we’ve assigned k variables correctly” “correctly”: the same way as x

Last slide: Randomly picking one of the two variables and flipping its value increases # correct assignments by 1 w.p. ≥ ½

Thus the algorithm is actually a random walk on the line of n+1 points, with Pr[going right] ≥ ½. Hitting time (i → n): O(n2)

So by repeating this flipping process O(n2) steps, we’ll reach x with high probability.

Algorithm 2: st-connectivity

st-Connectivity

Problem: Given a graph G and two vertices s and t on it, decide whether they are connected.

BFS/DFS (starting at s) solves the problem in linear time.

But uses linear space as well. Question: Can we use much less space?

Simple random walk algorithm A randomized algorithm takes O(log n)

space. Starting at s, perform the random walk for O(n3)

steps. If ever see t, output YES and stop. output NO.

Why it works? H(s,t) = O(n3). If s can reach t, then we should see it within O(n3)

time.

Key parameter 2: Mixing time

Convergence

Now let’s study the probability distribution of the particle after a long time.

What do we observe? The distribution converges to

uniform… wherever it starts.

Q: Does this hold in general?

u

v

w

t 0 1 0

1 1/2 0 1/2

2 1/4 2/4 1/4

3 3/8 2/8 3/8

4 5/16 6/16 5/16

5 11/32 10/32 11/32

6 21/64 22/64 21/64

Convergence

Well, Yes and No. Consider the following cases

on undirected graphs. Case 1: The graph is unconnected. Case 2: The graph is bipartite. [Thm] For any connected non-bipartite graph,

and any starting point, the random walk converges.

v2

v1

v3

v4 v5

v2v1

v3 v4

v5 v6

Converges to what?

In the previous triangle example: uniform. In general? As a result of the convergence, the

distribution doesn’t change by the matrix If the particle is on the graph according to the

distribution, then further random walk will result in the same distribution. We call it the stationary distribution.

[Fact] It’s unique.

u

v

w

stationary distribution

[Fact] It’s the following distribution: π(v) = d(v)/2m d(v) = degree of v, i.e. # of neighbors. m: |E|, i.e. # of edges.

[Proof] Consider one step of walk: π’(v) = ∑u: (u,v)∊E p(u)∙[1/d(u)]

= ∑u: (u,v)∊E [d(u)/2m]∙[1/d(u)] = ∑u: (u,v)∊E 1/2m = d(v)/2m = π(v)

So π is the stationary distribution. For regular graphs, π is the uniform distribution.

Proportional to degree

Note: the stationary distribution π(v) = d(v)/2m

is proportional to the degree of v. What’s the intuition? The more neighbors you

have, the more chance you’ll be reached. We will see another natural interpretation

shortly.

Speed of the convergence

We’ve seen that random walk converges to the stationary distribution.

Next question: how fast is the convergence? Let’s define the mixing time as

min {T: ∥pi(T) - π∥ ≤ ε}

where pi

(T): the distribution in time T, starting at i.∥∙∥: some norm.

We’ll see the reason of mixing later. Now let’s first see an example you run into everyday.

Algorithm 3: PageRank

PageRank

Google gives each webpage a number for its “importance”. [IT] Google: 10, Microsoft: 9, Apple: 9 [Media] NYTimes: 9, CNN: 10, sohu: 8, newsmth: 7 [Sports] NBA: 7, CBA: 7, CFA: 7 [University] MIT: 9, CUHK: 8, …

… Tsinghua, Pku, Fudan, IIT(B): 9

When you search for something by making a query, a large number of related webpages are retrieved. What webpages to retrieve? Information Retrieval. That’s

an orthogonal issue.

Ranking

How to give this big corpus to you? Search engines rank them based on the

“importance”, and give them in descending order. Thus presumably the first page contains the 10

most important webpages related to your query. Question: How to rank? PageRank: Use the vast link structure as in

indicator of an individual page’s value.

Reference system

Webpage A has a link to webpage BA thinks B is useful.

Think of it as A writing a recommendation letter for B. So a webpage with a lot of other pages pointing to it

is probably important. A guy getting a lot of letters is strong.

Further, pointers from pages that are themselves important bear more weight. Letters by Noga, László, Sasha, Avi, Andy, … mean a lot.

But the importance of those pages also need to be calculated… we have a recurrence equation.

Furthermore

If page A has a lot of links, then each link means less. Ok, you get Einstein’s letter, but you know what,

last year everyone on the market got his letter. So let’s assume that page A’s reference

weight is divided evenly to all pages B that A links to.

Recurrence equation: R(A) = ∑B: B→A R(B)/d(B)

where d(B) = # pages C that B links to

Sink issue

R(A) = ∑B: B→A R(B)/d(B) has a problem. There are some “sink” pages that contain no links to

other pages. Sinks accumulate weights without giving out. The recurrence equation only has solutions with weight on

sinks, losing the original intension of indicating the importance of all pages.

To handle this, we modify the recursion:R(A) = (1-α)/N + α ∑B: B→A R(B)/d(B)

Force each page to have a (1- α)-fraction of weights (evenly) going to all pages.

So each A also receives a weight of (1- α)/N from all pages α: around 0.85

Random Walk view

Note that the recursion

R(A) = (1-α)/N + α ∑B: B→A R(B)/d(B)

is exactly the random walk on the graph, where at each point A, we w/ prob. α, follow a random link; w/ prob. (1-α), go to a random page.

Question: How to solve this recurrence equation? # webpages: ~ 30 billion (and counting…)

Algorithm

Recall: the random walk converges to the stationary distribution! It’s a bit different since it’s random walk on directed graphs,

but this PageRank matrix has all good properties we need so that the random walk also converges to the solution.

Algorithm: start from any distribution, run a few iterations of random walk, and output the result. Google: 50-100 iterations, need a few days.

That should be close to the stationary distribution, which serves as indictor of the importance of pages.

Next

We’ll see a bit math behind the mixing story.

Mathematics behind the mixing [Eigenvalue decomposition] A symmetric matrix MNN

can be written as M = ∑i=1,…,n λiξiξiT

λi: eigenvalue. Order them: |λ1| ≥ |λ2| ≥ |λ3| ≥ … ≥ |λn| ξi: (column) eigenvector, i.e. Mξi = λiξi

The eigenvectors are orthonormal. ∥ξi∥2=1 ξi, ξj = 0, for all i≠j

M2 = (∑i=1,…,n λiξiξiT)(∑j=1,…,n λjξjξj

T)

= ∑i,j λiξiξiT λjξjξj

T = ∑i,j λi λj ξi,ξi ξjξjT

= ∑i λi2 ξjξi

T

Mt = ∑i λit ξjξi

T

Random walk in matrix form

A: adjacency matrix. (Aij = 1 if (i,j) ∊E; 0 o.w.) P: probability transition matrix.

Pij = 1/di if (i,j) ∊E; 0 o.w. P = D-1A, where D = diag(d1, …, dn). (di: deg(i)) For any distribution p, PTp is the distribution after

one step of random walk N = D-1/2AD-1/2 = D1/2PD-1/2

Nij = 1/(didj)1/2 if (i,j) ∊E; 0 o.w. N is symmetric, so N can be written as N = ∑i=1,…,n λiξiξi

T

Random Walk for t steps: Pt = (D-1/2ND1/2)t = D-1/2NtD1/2 = D-1/2 (∑iλi

tξiξiT)D1/2

= ∑iλitD-1/2ξiξi

TD1/2

Why mixing? Why to π?

[Thm] For connected non-bipartite graph, N has λ1 = 1 and ξ1 = π1/2 = ((d1/2m)1/2, …, (dn/2m)1/2) is the square

root version of the stationary distribution (so that the l2 norm is 1).

|λ2| < 1. (So is all other λi’s.)

Random Walk for t steps: Pt = ∑i λitD-1/2ξiξi

TD1/2

First item: λ1

tD-1/2ξ1ξ1TD1/2 = D-1/2(1/2m)[(didj)1/2]ijD1/2= (1/2m)[dj]ij

For any starting distribution p, (1/2m)[dj]ijT

p = π.

Speed of the convergence

All other items: since |λi|<1, λitD-1/2ξiξi

TD1/2→0! Speed of convergence depends on how close |λ2|

is to 1.

PageRank matrix: |λ2| = α (≈ 0.85)

Expander: 1-|λ2| = Ω(1)

The rest of the talk

Only ideas are given.

Many details are omitted.

I may cheat a bit to illustrate the main steps.

Algorithm 4: Approximately counting

Algorithm 4: Approximately counting Task: estimate the size of an exponentially large set

V Example: Given G, count # of perfect matchings.

Approach: find a chain V0 V⊆ 1 … V⊆ ⊆ m=V |V0| is easy to compute; m = poly(n) layers; Each ratio |Vi+1|/|Vi| = poly(n) is easy to estimate.

Then |V| = |V0| (|V1|/|V0|) (|V2|/|V1|)… (|Vn|/|Vn-1|) can be estimated.

Question: How to estimate |Vi+1|/|Vi|?

Estimate the ratio

Estimate by random sampling: Generate a random element uniformly distributed

in Vi+1, see how often it hits Vi. Question: How to uniformly sample Vi+1?

By random walk We construct a regular graph with vertex set Vi+1

The algorithm runs efficiently (i.e. in poly(n) time) if the walk converges to uniform rapidly (i.e. in poly(n) time).

It’s the case in perfect matching counting problem.

Algorithm 5: Error Reduction with efficient randomness

Error reduction

Task: Reduce the error of a randomized algorithm A. Error: Prr {0,1}^∈ m[r is bad for A] = 1/3 → ε?

Naïve approach: Draw k random strings r1, …, rk,

Run algorithm A using r1, …, rk and get k answers Output the majority of the answers

[Fact] the error drops to 2-Θ(k) . --- Chernoff’s bound Randomness complexity: mk. Question: Can we reduce the error prob w/o using too

many additional random bits?

B

{0,1}m

Expander graph

Expander: Eigenvalue gap is large --- Ω(1) Random walk converges very fast --- O(log n). Highly connected --- diameter O(log n) Large boundary --- any subset has lots of edges

going out Could be sparse --- ∃ constant degree expanders.

We know how to construct them explicitly.

Algorithm for error reduction

New Algorithm A’: Construct an expander with V = {0,1}m

Start from a random vertex r1

Perform a random walk (r1, r2,…, rk) of length k

Random algorithm A using r1, r2,…, rk and get k answers Output the majority of the answers

Thm: the error prob of A’ is also 2-Θ(k)

How many random bits used? m + O(k) The expander is of constant degree.

Highly dependent! Why it works?

B

{0,1}m

Expander: large boundary

For simplicity, consider the one-side error case: Algorithm A’ wrong ↔

Whole walk in B. Expander: Every set has

a large boundary. At every step, it walks

outside B w.p. Ω(1). Pr[k steps never out] =

2-Θ(k)

Non-expander

B

Boundary

{0,1}m: all random strings

PART II. Quantum Walk

Quantum mechanics in one slide

1

0

α|0+β|1(|α|2+|β|2=1)

A classical bit

A quantum bit (qubit)

MathPhysics

Tensor ProductComposition

ProjectionMeasurement

Unitary MatrixEvolution

Unit VectorPhysical Systemα

β

Measure by |0 and |1: - get 0 w.p. |α|2; system → |0; - get 1 w.p. |β|2; system → |1.

1

0

1

0

Classical:

Quantum:|1

|0

|1

|0

State space for 2 bits: all combinations {00, 01, 10, 11}

State space for 2 qubits: the space span{|00,|01,|10,|11}

|

|

α,β: amplitudes

Quantum walk

Many things become quite tricky.

Even the definition of quantum walk.

We’ll ignore the formal definitions here, but only present some results.

Type 1: Discrete Quantum Walk

Coin register

The most natural quantum counterpart of random walk is the following: each vertex v gives its neighbors (equally) some “mass” so that the l2 norm of the total mass is still 1.

However, simply accumulating all mass from neighbors does not work The operator is not unitary.

To overcome this, we need a register to remember where the mass is from.

It amounts to add a coin register. Flip the quantum coin: |v|c → |vH|c, where H = Walk accordingly: |v|c → |nc(v)|c, where nc(v)

is the cth neighbor of v.

1p2

µ1 11 ¡ 1

¶

General graph

Given any random walk on an undirected graph G, Szegedy proposed a method to transform it into a quantum walk

Set hitting time: A a target set T⊆ V is hidden in the graph Starting from the uniform distribution, what’s the expected

time to hit some point in T? Classical set hitting time = O((ε∆)-1)

ε : density of the target set, i.e. |T|/|V| ∆: eigenvalue gap of matrix P

[Szegedy] Quantum set hitting time = O(√Classical set hitting time) = O((ε∆)-1/2)

T

V

Quantum algorithm for Element Distinctness Element Distinctness: To decide whether all input

integers are distinct. [Ambainis] Discrete quantum walk on the following

graph: (r = n2/3) V = {S⊆[n]: |S| = r}; E = {(S,T): |S∩T| = r-2}

Eigenvalue gap: ∆ = 1/r Target set density: ε = r2/n2

Quantum running time = setup time + hitting time = O(r) + O((ε∆)-1/2) = O(r + n/√r) = O(n2/3).

Type 2: Continuous Quantum Walk

Continuous quantum walk on a graph Given an undirected graph G, and a

Hermitian matrix H that respects the graph structure Hermitian: (HT)* = H. Respect G: Hij = 0 if (i,j)∉E.

The dynamics ψ(t) = ∑j αi(t)|i for H is governed by the Schrodinger equation

i(d/dt) αi(t) = ∑j:(i,j)∊EHijαj(t),

which has solution |ψ(t) = eiHt |ψ(0).

Formula Evaluation

Grover Search: find a marked point in an n-point set in time O(n1/2). Equivalently: evaluate OR in time O(n1/2).

Generalizations:

∧

∨∨

∧

¬∨

∧

(Balanced) AND-OR Tree

General AND-OR-NOT Formula (General Game Tree)

Formula evaluation

Motivation: a natural generalization of OR --- Grover search well studied subject in TCS matching lower bound known long ago game: min-max tree.

Same up to log(n)

minmin

max

Breakthroughs

[FGG, 07] An O(n1/2) algorithm for Balanced AND OR Tree. Method: scattering theory… Not really understandable for computer scientists…

[CRSZ, A, FOCS’07] An O(n1/2+o(1)) algorithm for General AND OR NOT Formula. Method: phase estimation + quantum walk. Simpler algorithm, simpler proof. For special cases like Balanced AND OR Tree: O(n1/2)

Classical implications

[OS, STOC’03] Any formula f of size n has polynomial threshold function thr(f) = O(n1/2)?

Our result: Q( f ) ≤ n1/2+o(1)

[KlivansServedio, STOC’01; KOS, FOCS’02] Class C of Boolean functions has thr(f) ≤ r for all fC, ⇒ C

can be learned in time nO(r) (in both PAC or adversarially generated examples)

[Implication] Formulas are learnable in time 2 This is very interesting because studies of quantum

algorithms solve a purely classical open problem! This is not uncommon in quantum computing!

~thrthr( ( f f ) ) ≤≤ degdeg( ( f f ) ) ≤≤

nn1/2+o(1)1/2+o(1)

[BBCMdW, JACM’01]

Definition of thr

Sketch of the algorithm

{AND,OR,NOT}⇔{NAND} For the formula f and an input x, construct a

graph Gx

Let A be the adjacency matrix of Gx; consider A’s spectrum.

x1 x2

x3 x4 x5

:0 :1

:0 :1 :1

Key observation

Function values and amplitudes of 0-eigenvector propagate in the same way.

∧∧

∧0

• Red: function value at a vertex• green: amplitude of 0-eigenvector

1 1

00 ≠0 ≠0

≠0

If a function evaluates to 0, then an 0-eigenvector

∧∧

∧1

0

11 =0 =0

=0

If a function evaluates to 1, then 0-eigenvector has no support on the root.

Phase Estimation

Thus qualitatively, it’s enough to know whether there is a 0-eigenvector with support on root.

Phase estimation: Given an unitary operator U (to use) and an eigenvector |ψ, find the phase θ in the corresponding eigenvalue eiθ. Unitary matrix: all eigenvalues have l2-norm 1

For eiHt, the phase is nothing but the corresponding eigenvalue of H.

Thus use phase estimation, we can find an eigenvalue of H. Depending on whether it’s 0, output the answer to f(x).

The quantum walk is here!

A highlevel structure of algorithm We need to carefully choose weight hij on edges (i,j)

s.t. the above qualitative connection also works quantitatively. For f(x) = 0,

an 0-eigenvector with Ω(1) support on root. For f(x) = 1,

eigenvector with support on root has eigenvalue Ω(n-1/2) Algorithm: Starting from the root, use quantum walk

to carry out the Phase Estimation, output the answer depending on whether the phase is 0.

Summary

Random walk is a simple but powerful tool. There will sure be more important algorithmic

applications to be found.

Theories of quantum walk have been rapidly developed in the past couple of years.

A lot of fundamental issues yet to be better understood.

Significant breakthrough algorithms using quantum walk are ahead!

Reference

For random walk on graphs: Lovász, Random Walks on Graphs: A Survey,

Combinatorics, 353-398, 1996. Chung, Spectral Graph Theory, American Mathematical

Society, 1997. For quantum walk:

Quantum computing in general: Textbook by Nielsen and Chuang Lecture notes by Vazirani, by Ambainis, and by Childs

Quantum walk: Surveys by Kempe and by Santha. All above lectures have nice chapters for quantum walk.

Thanks!