Spectral Partitioning in the Planted Partition Model

Transcription

1 Spectral Graph Theory Lecture 21 Spectral Partitioig i the Plated Partitio Model Daiel A. Spielma November 11, Itroductio I this lecture, we will perform a crude aalysis of the performace of spectral partitioig algorithms i the plated partitio model. I this model, we build a radom graph that has a atural partitio. The simplest model of this form is for the graph bisectio problem. This is the problem of partitioig the vertices of a graph ito two equal-sized sets while miimizig the umber of edges bridgig the sets. To create a istace of the plated bisectio problem, we first choose a parititio of the vertices ito equal-sized sets V 1 ad V 2. Whe the choose probabilities p > q, ad place edges betwee vertices with the followig probabilities: p if u V 1 ad v V 1 Pr [(u, v) E] = p if u V 2 ad v V 2 q otherwise. The expected umber of edges crossig betwee V 1 ad V 2 will be q V 1 V 2. If p is sufficietly larger tha q, the every other bisectio will have more crossig edges. I this lecture, we will show that this partitio ca be recovered from the secod eigevector of the adjacecy matrix of the graph. This will be a crude versio of a aalysis of McSherry [McS01] There have bee may aalyses of graph partitioig algorithms uder plated partitio models such as this. The model is motivated by the idea that vertices (or geeral items) belog to certai categories, ad that vertices i the same categories are more likely to be coected. Such models also arise i the aalysis of clusterig algorithms. However, it is ot clear that these models represet practice very well The Perturbatio Approach As log as we do t tell our algorithm, we ca choose V 1 = {1,..., /2} ad V 2 = {/2 + 1,..., }. Let s do this for simplicity. 21-1

2 Lecture 21: November 11, Defie the matrix p p q q.. [ ] M = p p q q pj/2 qj q q p p = /2, qj /2 pj /2.. q q p p where we write J /2 for the square all-1s matrix of size /2. The adjacecy matrix of the plated partitio graph is obtaied by settig A(i, j) = 1 with probability M(i, j), subject to A(i, j) = A(j, i). So, this is a radom graph, but the probabilities of some edges are differet from others. We will study a very simple algorithm for fidig a approximatio of the plated bisectio: compute v 2, the eigevector of the secod-largest eigevalue of A. The, set S = {i : v 2 (i) < 0}. We guess that S is oe of the sets i the bisectio. We will show that uder reasoable coditios o p ad q, S will be mostly right. Ituitively, the reaso is that A is a slight perturbatio of M, ad so the eigevectors of A should look like the eigevectors of M. For that to make sese, I should have said what the eigevectors M look like. costat vectors are eigevectors of M. We have M1 = (p + q)1. 2 Of course, the The secod eigevector of M has two values: oe o V 1 ad oe o V 2. Let s be careful to make this a uit vector. We take { 1 i V 1 w 2 (i) = 1 i V 2. The, Mv 2 = 2 (p q)w 2. So, the secod-largest eigevalue of M is (/2)(p q). As M has rak 2, all the other eigevalues of M are zero. Now, let R = A M. For (u, v) i the same compoet, ad for (u, v) i differet compoets, Pr [R(u, v) = 1 p] = p Pr [R(u, v) = p] = 1 p, Pr [R(u, v) = 1 q] = q Pr [R(u, v) = q] = 1 q. As i the last lecture, we ca boud the probability that the orm of R is large. While I m ot yet sure if we ca boud it usig the same techique, we ca appeal to a result of Vu [Vu07, Theorem 1.4], which implies the followig. ad ad

3 Lecture 21: November 11, Theorem There exist costats c 1 ad c 2 such that with probability approachig 1, R 2 p + c 1 (p) 1/4 l, provided that p c 2 l 4. We apply the followig corollary. Corollary There exists a costat c 0 such that with probability approachig 1, R 3 p, provided that p c 0 l 4. I fact, Krivelevich ad Vu [?, Theorem??] prove that the probability that the orm of R exceeds this value by more tha t is expoetially small i t. However, we will ot eed that fact for this lecture. Igorig the details of the asymptotics, let s just assume that R is small, ad ivestigate the cosequeces Perturbatio Theory for Eigevectors Let α 1 α 2 α be the eigevalues of A, ad let µ 1 > µ 2 > µ 3 = = µ be the eigevalues of M. We kow from a problem set that α i µ i R. I particular, if the ad, assumig q > p/3, we have R < (p q), 4 4 (p q) < α 2 < 3 (p q) 4 α 1 > 3 (p q). 4 So, we ca view α 2 as a perturbatio of µ 2. The atural questio is whether we ca view w 2 as a perturbatio of v 2. Here is the theory that says we ca.

4 Lecture 21: November 11, Theorem Let A ad M be symmetric matrices. Let R = M A. Let α 1 α be the eigevalues of A with correspodig eigevectors v 1,..., v ad let Let µ 1 µ be the eigevalues of M with correspodig eigevectors w 1,..., w. Let θ i be the agle betwee v i ad w i. The, 2 R si θ i mi j i α i α j, ad si θ i 2 R mi j i µ i µ j. We remark that this boud may be tighteed slightly, essetially elimiatig the 2. We defer the proof of this theorem for a few miutes, ad first see what it implies Partitioig Cosider δ = v 2 w 2. For every vertex i that is mis-classified by v 2, we have δ(i) 1. So, if v 2 mis-classifies k vertices, the k δ. As w ad v are uit vectors, we may apply the crude iequality (the 2 disappears as θ 2 gets small). δ 2 si θ 2 To combie this with the perturbatio boud, we assume q > p/3, ad fid Assumig that R 3 p, we fid mi µ 2 µ j = (p q). j 2 2 si θ 2 3 p 2 (p q) = 6 p. (p q) So, the umber k of misclassified vertices satisfies k 6 p, (p q) which implies k 36p (p q) 2.

5 Lecture 21: November 11, So, if p ad q are both costats, we expect to misclassify at most a costat umber of vertices. If p = 1/2, ad q = p 12/, the we get 36p (p q) 2 = 8, so we expect to mis-classify at most a costat fractio of the vertices Proof of Eigevector Perturbatio Proof of Theorem By cosiderig the matrices M λ i I ad A µ i I istead of M ad A, we ca assume that µ i = 0. As the theorem is vacuous if µ i has multiplicity more tha 1, we may also assume that µ i has multiplicity 1 as a eigevalue, ad that w i is a uit vector i the ullspace of M. Our assumptio that µ i = 0 also leads to λ i R. Expad v i i the eigebasis of M, as v i = j c j w j, where c j = w T j v i. Settig we may compute δ = mi j i µ j, Mv i 2 = j c 2 jµ 2 j j i c 2 jδ 2 = δ 2 j i c 2 j = δ 2 (1 c 2 i ) = δ 2 si 2 θ i. O the other had, So, Mv i Av i + Rv i = λ i + Rv i 2 R. si θ i 2 R. δ

6 Lecture 21: November 11, It may seem surprisig that the amout by which eigevectors move depeds upo how close their respective eigevalues are to the other eigevalues. However, this depedece is ecessary. To see why, first cosider a matrix with a repeated eigevalue, such as [ ] 1 0 A =. 0 1 Now, let v be ay uit vector, ad cosider B = A + ɛvv T. The matrix B will have v as a eigevector of eigevalue 1 + ɛ as well as a eigevalue of 1. So, by makig a arbitrarily small perturbatio, we were able to select which eigevalue of B was largest. To make this effect clearer, let w be ay other uit vector, ad cosider the matrix C = A + ɛww T. So, w is the eigevector of C of eigevalue (1 + ɛ), ad the other eigevalue is 1. O the other had, C B ɛww T + ɛww T = 2ɛ. So, while B ad C differ very little, their domiat eigevectors ca be completely differet. This is because the eigevalues were close together Improvig the Partitio If I get a chace, I ll describe how oe improves such a partitio i practice, ad how McSherry did it i theory. I ll begi by observig that the aalysis we performed is very pessimistic. It relies o a upper boud o w 2 v 2. But, v 2 was produced by a radom process. So, it seems ulikely that all of its weight would be cocetrated o a few vertices. Refereces [McS01] F. McSherry. Spectral partitioig of radom graphs. I FOCS 01: Proceedigs of the 42d IEEE symposium o Foudatios of Computer Sciece, page 529, Washigto, DC, USA, IEEE Computer Society. [Vu07] Va Vu. Spectral orm of radom matrices. Combiatorica, 27(6): , 2007.