Inthispaper,weareinterestedinrandomgraphswithaxeddegree



Similar documents
DESIGNINGCRYPTOGRAPHICPOSTAGEINDICIA. NevinHEINTZE BellLaboratories 600MountainAve,MurrayHillNJ07974,USA

Solutions to Homework 6

A REMARK ON ALMOST MOORE DIGRAPHS OF DEGREE THREE. 1. Introduction and Preliminaries

Lecture 4: BK inequality 27th August and 6th September, 2007

Lecture 5/6. Enrico Bini

Analysis of Algorithms I: Optimal Binary Search Trees

Rising Rates in Random institute (R&I)

On an anti-ramsey type result

Scheduling Shop Scheduling. Tim Nieberg

The distribution of second degrees in the Buckley Osthus random graph model

Class One: Degree Sequences

Finding and counting given length cycles

Ramsey numbers for bipartite graphs with small bandwidth

SYMMETRIC ENCRYPTION. Mihir Bellare UCSD 1

8.1 Min Degree Spanning Tree

Note on some explicit formulae for twin prime counting function


SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH

* Research supported in part by KBN Grant No. PBZ KBN 016/P03/1999.

Single machine models: Maximum Lateness -12- Approximation ratio for EDD for problem 1 r j,d j < 0 L max. structure of a schedule Q...

Linear Programming I

Connectivity and cuts

Hardware Assisted Virtualization

On the k-path cover problem for cacti

A simple criterion on degree sequences of graphs

Introduction to Scheduling Theory

URBANA HIGH SCHOOL SUMMER SCHOOL Urbana School District #116. Summer School Principal Mr. Michael Gourley (217)

Odd induced subgraphs in graphs of maximum degree three

Correspondence analysis for strong three-valued logic

WinCC OA Partner Program. Highest quality for our customers

A Sublinear Bipartiteness Tester for Bounded Degree Graphs

x a x 2 (1 + x 2 ) n.

The number of generalized balanced lines

Data Structures Fibonacci Heaps, Amortized Analysis

Follow the Perturbed Leader

The risks of mesothelioma and lung cancer in relation to relatively lowlevel exposures to different forms of asbestos

Cycles and clique-minors in expanders

ON THE COMPLEXITY OF THE GAME OF SET.

Trigonometry Hard Problems

Network (Tree) Topology Inference Based on Prüfer Sequence

FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES

The Ergodic Theorem and randomness

THE NUMBER OF GRAPHS AND A RANDOM GRAPH WITH A GIVEN DEGREE SEQUENCE. Alexander Barvinok

Using GeoGebra to create applets for visualization and exploration.

FIND ALL LONGER AND SHORTER BOUNDARY DURATION VECTORS UNDER PROJECT TIME AND BUDGET CONSTRAINTS

Dynamic TCP Acknowledgement: Penalizing Long Delays

Exponentially concave functions and multiplicative cyclical monotonicity

A generalized allocation scheme

/ /A /0001/A20438/ /0000/PRINT DATE: 6/03/06

Cycles in a Graph Whose Lengths Differ by One or Two

SEQUENCES OF MAXIMAL DEGREE VERTICES IN GRAPHS. Nickolay Khadzhiivanov, Nedyalko Nenov

Single machine parallel batch scheduling with unbounded capacity

Formal Modeling and Reasoning about the Android Security Framework

VIRGINIA MASON PRODUCTION SYSTEM (VMPS)

Math 55: Discrete Mathematics

Objectives. Distributed Databases and Client/Server Architecture. Distributed Database. Data Fragmentation

Metric Spaces. Chapter 1

ONLINE DEGREE-BOUNDED STEINER NETWORK DESIGN. Sina Dehghani Saeed Seddighin Ali Shafahi Fall 2015

SPERNER S LEMMA AND BROUWER S FIXED POINT THEOREM

Reliability Guarantees in Automata Based Scheduling for Embedded Control Software

Path Querying on Graph Databases

with functions, expressions and equations which follow in units 3 and 4.

Topology-based network security

Quantum Physics II (8.05) Fall 2013 Assignment 4

Reinforcement Learning

Matrix-Chain Multiplication

CSC2420 Fall 2012: Algorithm Design, Analysis and Theory

Reinforcement Learning

INDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS

SEMITOTAL AND TOTAL BLOCK-CUTVERTEX GRAPH

OPTIMAL SELECTION BASED ON RELATIVE RANK* (the "Secretary Problem")

AT&Tsalesdataset:theneedistostoreamultiGiga-bytematrixon-line,withcustomersforrows,daysforcolumns,andamountspentineachcellofthematrix.

0 0 such that f x L whenever x a

Master's projects at ITMO University. Daniil Chivilikhin PhD ITMO University

Topological properties of Rauzy fractals

1 Derivation of the cost function: The production

Towards Interface Types for Haskell

CIRCLE COORDINATE GEOMETRY

OPERATING SYSTEM - VIRTUAL MEMORY

Warm-up: Compound vs. Annuity!

Section 4.2: The Division Algorithm and Greatest Common Divisors

Cryptography for the Cloud

PRODUCTION PLANNING AND SCHEDULING Part 1

DEGREES OF ORDERS ON TORSION-FREE ABELIAN GROUPS

Approximating the Minimum Chain Completion problem

The Social Hourglass: Enabling Socially- aware Applications and Services. Adriana Iamnitchi University of South Florida

Generating Elementary Combinatorial Objects

A Graph-Theoretic Network Security Game

Intro to Ancient Writing Systems

CSC 505, Fall 2000: Week 8

The degrees of freedom of the Lasso in underdetermined linear regression models

AGraphDrawingandTranslationServiceon StinaBridgeman,AshimGargandRobertoTamassia DepartmentofComputerScience thewww*

Cardiovascular Practice Quality Improvement : Role of ACC-NCDR and HIT

LOGISTIQUE ET PRODUCTION SUPPLY CHAIN & OPERATIONS MANAGEMENT

Breaking The Code. Ryan Lowe. Ryan Lowe is currently a Ball State senior with a double major in Computer Science and Mathematics and

4. Expanding dynamical systems

On the crossing number of K m,n

Line Plots. Objective To provide experience creating and interpreting line plots with fractional units. Assessment Management

I live in Hertfordshire but my child attends school in another county. What can I apply for?

Oral Diagnosis: The Physical Exam

Transcription:

ACRITICALPOINTFORRANDOM GRAPHSWITHAGIVENDEGREE DepartmentofMathematics PittsburghPA15213,U.S.A. Carnegie-MellonUniversity SEQUENCE MichaelMolloy UniversitePierreetMarieCurie EquipeCombinatoire BruceReed August14,2000 Paris,France CNRS thenalmostsurelyallcomponentsinsuchgraphsaresmall.wecan sumto1,weconsiderrandomgraphshavingapproximatelyinverticesofdegreei.essentially,weshowthatifpi(i?2)i>0thensuch Givenasequenceofnon-negativerealnumbers0;1;:::which Abstract graphsalmostsurelyhaveagiantcomponent,whileifpi(i?2)i<0 applytheseresultstogn;p;gn;m,andotherwell-knownmodelsof randomgraphs.therearealsoapplicationsrelatedtothechromatic numberofsparserandomgraphs. 1

berofverticesandthenumberofcyclesinthelargestcomponent.ofcourse, fromwhichthegraphsarepicked.inonestandardmodelwepickarandom thebehaviouroftheseparametersdependsontheprobabilitydistribution Inthispaperweconsidertwoparametersofcertainrandomgraphs:thenum- graphgn;mwithnverticesandmedgeswhereeachsuchgraphisequally 1IntroductionandOverview ofnandletngotoinnity.thepointm=12nisreferredtoasthecritical likely.weareinterestedinwhathappenswhenwechoosemasafunction size(n2=3).ifm=cnforc>12thenthereareconstants;>0dependent probabilitytendingtooneasntendstoinnity)gn;mhasnocomponentof pointorthedouble-jumpthresholdbecauseofclassicalresultsduetoerd}os oncsuchthata.s.gn;mhasacomponentonatleastnverticeswithat sizegreaterthano(logn),andnocomponenthasmorethanonecycle.if M=12n+o(n),thenalmostsurely(a.s.)thelargestcomponentofGn;Mhas andrenyi[8]concerningthedramaticchangeswhichoccurtotheseparametersatthispoint.ifm=cn+o(n)forc<12thenalmostsurely(i.e.with leastncycles,andnoothercomponenthasmorethano(logn)verticesor see[3],[11],or[14]. probability.ofcourse,wehavetosaywhatwemeanbyadegreesequence. sequencewhereeachgraphwiththatdegreesequenceischosenwithequal Ifthenumberofverticesinourgraph,nisxed,thenadegreesequenceis ofgn;m.formorespecicsonthesetwoparametersatandaroundm=12n morethanonecycle.thiscomponentisreferredtoasthegiantcomponent simplyasequenceofnnumbers.however,weareconcernedherewithwhat quenceofsequences".thus,wegeneralizethedenitionofdegreesequence: happensasymptoticallyasntendstoinnity,sowehavetolookata\se- Inthispaper,weareinterestedinrandomgraphswithaxeddegree valuedfunctionsd=d0(n);d1(n);:::suchthat 2.Pi0di(n)=n. 1.di(n)=0forin; GivenanasymptoticdegreesequenceD,wesetDntobethedegree Denition:Anasymptoticdegreesequenceisasequenceofinteger- sequencefc1;c2;:::;cng,wherecjcj+1andjfj:cj=igj=di(n)foreach i0..denedntobethesetofallgraphswithvertexset[n]withdegree 2

sequencedn.arandomgraphonnverticeswithdegreesequencedisa degreesequenced,wewantthesequencesdntobeinsomesensesimilar. alln1. uniformlyrandommemberofdn. Denition:AnasymptoticdegreesequenceDisfeasibleifDn6=;for constantsisuchthatlimn!1di(n)=n=i. Wedothisbyinsistingthatforanyxedi,theproportionofverticesof degreeiisroughlythesameineachsequence. Becausewewishtodiscussasymptoticpropertiesofrandomgraphswith Inthispaper,wewillonlydiscussfeasibledegreesequences. andweonlyclaimthingstobetrueforsucientlylargen. beenrandomregulargraphs.perhapsthemostimportantrecentresultisby Denition:AnasymptoticdegreesequenceDissmoothifthereexist graphforanyconstantk3,thengisa.s.hamiltonian. RobinsonandWormald[20],[21],whoprovedthatifGisarandomk-regular Throughoutthispaper,allasymptoticswillbetakenasntendsto1 quencecomesfromtheanalysisofthechromaticnumberofsparserandom graphs.thisisbecauseaminimally(r+1)-chromaticgraphmusthavemini- Inthepast,themostcommonlystudiedrandomgraphsofthistypehave mumdegreeatleastr.inanattempttodeterminehowmanyedgeswerenec- iedtheexpectednumberofsubgraphsofminimumdegreethreeinrandom cessarytoforcearandomgraphtoa.s.benot3-colourable,chvatal[7]stud- graphswithalinearnumberofedges.heshowedthatforc<c=1:442:::, Anothermotivationforstudyingrandomgraphsonaxeddegreese- theprobabilitythatarandomgraphonnverticeswithminimumdegree authorsusedaspecialcaseofthemaintheoremofthispapertoshowthat threeandatmost1:793nedgesisminimally4-chromaticisexponentially ponentiallylarge.intheworkthatmotivatedtheresultsofthispaper,the whileforc>ctheexpectednumberofsuchsubgraphsingn;m=cnisex- small[18].weusedthistoshowthatforcalittlebitbiggerthanc,the theexpectednumberofsuchsubgraphsingn;m=cnisexponentiallysmall, nentiallysmall.thissuggeststhatdeterminingtheminimumvalueofcfor expectednumberofminimally4-chromaticsubgraphsofgn;m=cnisexpo- whicharandomgraphwithcnedgesisa.s.4-chromaticmayrequiremore thanastudyofthesubgraphswithminimumdegree3. Recently Luczak[14]showed(amongotherthings)thatifGisarandom 3

thenallthecomponentsofsucharandomgrapharea.s.quitesmall.note giantcomponent.ourmaintheoremalsogeneralizesthisresult. graphonaxeddegreesequence1,withnoverticesofdegreelessthan2, howcloselythisparallelsthephenomenoninthemorestandardmodelgn;m. andatleast(n)verticesofdegreegreaterthan2,thenga.s.hasaunique foranymodelofrandomgraphsaslongas:(i)wecandeterminethedegree sequenceofgraphsinthemodelwithreasonableaccuracy,and(ii)oncethe graphwithdegreesequenceda.s.hasagiantcomponent,whileifq(d)<0 Notefurtherthattheseresultsallowustodetermineasimilarthreshold WesetQ(D)=Pi1i(i?2)i.EssentiallyifQ(D)>0thenarandom degreesequenceisdetermined,everygraphonthatdegreesequenceisequally likely.gn;pissuchamodel,andthus(asweseelater),ourresultscanbe Dnhas(i+o(1))nverticesofdegreeiforeachi0.Pickarandomvertex usedtoverifythepreviouslyknownthresholdforgn;p. inourgraphandexposethecomponentinwhichitliesusingabranching itsneighbours,repeatinguntiltheentirecomponentisexposed.nowwhen ofwhyitdetermineswhetherornotagiantcomponentexists.supposethat process.inotherwords,exposeitsneighbours,andthentheneighboursof avertexofdegreeiisexposed,thenthenumberof\unknown"neighbours Beforedeningtheparameterprecisely,wegiveanintuitiveexplanation increasesbyi?2.theprobabilitythatacertainvertexisselectedasa neighbourisproportionaltoitsdegree.thereforetheexpectedincreasein thenumberofunknownneighboursis(roughly)pi1i(i?2)i.thisis,of course,q(d). quickly.however,ifitispositivethenthenumberofunknownneighbours, formally. mainthrustofourarguments.wewillnowbegintostateallofthismore andthusthesizeofthecomponent,mightgrowquitelarge.thisgivesthe Thus,ifQ(D)isnegativethenthecomponentwilla.s.beexposedvery translate. insistthattheasymptoticdegreesequencesweconsiderarewell-behaved. Inparticular,whenthemaximumdegreeinourdegreesequencegrowswith n,wecanrunintosomeproblemsifthingsdonotconvergeuniformly.for example,ifd1(n)=n?dn:9e;di(n)=dn:9eifi=dpne,anddi(n)=0 1Hedidn'tusetheasymptoticdegreesequenceintroducedhere,buttheresults Thereareafewcaveats,soinorderforourresultstohold,wemust 4

However,thisisdeceivingasthereareenoughverticesofdegreepntoensure otherwise,then1=1,andi=0fori>1,andwegetq(d)=?1. thatagiantcomponentcontainingn?o(n)verticesa.s.exists. 1.Disfeasibleandsmooth. 2.i(i?2)di(n)=ntendsuniformlytoi(i?2)i;i.e.forall>0there Denition:AnasymptoticdegreesequenceDiswell-behavedif: 3. existsnsuchthatforalln>nandforalli0: L(D)=lim ji(i?2)di(n) n n!1xi1i(i?2)di(n)=n?i(i?2)ij< exists,andthesumapproachesthelimituniformly,i.e.: (a)ifl(d)isnitethenforall>0,thereexistsi;nsuchthatfor (b)ifl(d)isinnitethenforallt>0,thereexistsi;nsuchthat alln>n: foralln>n: jixi=1i(i?2)di(n)=n?l(d)j< WenotethatitisaneasyexercisetoshowthatifDiswell-behavedthen: L(D)=Q(D) ixi=1i(i?2)di(n)=n>t assparse: numberofedgesinourdegreesequence.wedenesuchadegreesequence Itisnotsurprisingthatthethresholdoccurswhentherearealinear 5

K+o(1)forsomeconstantK. Denition:AnasymptoticdegreesequenceDissparseifPi0idi(n)=n= chosenuniformlyatrandomfromamongstallsuchgraphs.then: di(n)=0.letgbeagraphwithnvertices,di(n)ofwhichhavedegreei, nitethendissparse. degreesequenceforwhichthereexists>0suchthatforallnandi>n14?, Themainresultinthispaperisthefollowing: Notethatforawell-behavedasymptoticdegreesequenceD,ifQ(D)is Theorem1LetD=d0(n);d1(n);:::beawell-behavedsparseasymptotic (a)ifq(d)>0thenthereexistconstants1;2>0dependentond (b)ifq(d)<0andforsomefunction0!(n)n18?,di(n)=0 foralli!(n),thenforsomeconstantrdependentonq(d),g dependentond. suchthatga.s.hasacomponentwithatleast1nverticesand hasfewerthan2r!(n)2logncycles.also,a.s.nocomponentof a.s.hasnocomponentwithatleastr!(n)2lognvertices,anda.s. onecomponentofsizegreaterthanlognforsomeconstant 2ncycles.Furthermore,ifQ(D)isnitethenGa.s.hasexactly analogoustothecasec=1inthemodelgn;p=c=n,andwouldbeinteresting Theorem1(a)agiantcomponent. NotealsothatTheorem1failstocoverthecasewhereQ(D)=0.Thisis NotethatifQ(D)<0thenQ(D)isnite. ConsistentwiththemodelGn;M,wecallthecomponentreferedtoin Ghasmorethanonecycle. toanalyze. onaxedwell-behaveddegreesequencewithcn+o(n)edgesforanyc>1 isthatitisdiculttogeneratesuchgraphsdirectly.insteadishasbecome standardtostudyrandomcongurationsonaxeddegreesequence,and Pi1ii>2;Pi1i(i?2)i<0;Pi1i=1;0i1. thenga.s.hasagiantcomponent,asthereisnosolutionto OneimmediateapplicationofTheorem1isthatifGisarandomgraph usesomelemmaswhichallowustotranslateresultsfromonemodeltothe andrenedbybollobas[3]andalsowormald[22]. other.thecongurationmodelwasintroducedbybenderandcaneld[2] Amajordicultyinthestudyofrandomgraphsonxeddegreesequences 6

degreesequence,wedothefollowing: 1.FormasetLcontainingdeg(v)distinctcopiesofeachvertexv. 2.ChoosearandommatchingoftheelementsofL. Inordertogeneratearandomcongurationwithnverticesandaxed denedbythepairsinthematching.wesaythatacongurationhasa graphicalpropertypifitsunderlyingmultigraphdoes. ofarandomcongurationonadegreesequencemeetingtheconditionsof Theorem1issimplewithprobabilitytendingtoe?(D),forsome (D)<O(n1=2?).Theconditiondi(n)=0foralli>n1=4?isneededto Eachcongurationrepresentsanunderlyingmultigraphwhoseedgesare applythisresult.ifq(d)isnitethen(d)tendstoaconstant. urations,whichisclearlyequalforallgraphsonthesamedegreesequence Usingthemainresultin[17]itfollowsthattheunderlyingmultigraph andthesamenumberofvertices. propertypwithprobabilityatleast1?znforsomeconstantz<1,thena meetingtheconditionsoftheorem1(withq(d)possiblyunbounded)hasa Thisgivesusthefollowingveryusefullemmas: Also,anysimplegraphGcanberepresentedbyQv2V(G)deg(v)!cong- thenarandomgraphwiththesamedegreesequencea.s.hasp. meetingtheconditionsoftheorem1a.s.hasapropertyp,andifq(d)<1, Lemma2IfarandomcongurationwithagivendegreesequenceD Lemma1IfarandomcongurationwithagivendegreesequenceD conguration. opedindependentlybybollobasandfrieze[6],flajolet,knuthandpittel graphsonagivendegreesequence. [10],andChvatal[7].Bothmodelsareveryusefulwhenworkingwithrandom Thecongurationmodelisverysimilartothepseudographmodeldevel- UsingtheseLemmas,itwillbeenoughtoproveTheorem1forarandom inwhichwearestudyingthem,wecannowgiveamoreformaloverviewof theproof.theremainderofthissectionisdevotedtothisoverview.inthe seesomeapplicationsoftheorem1:theaforementionedworkconcerningthe followingtwosectionswegiveallthedetailsoftheproof.insection4,we Havingdenedthepreciseobjectsthatweareinterestedin,andthemodel 7

bemorespecicregardingtheorderinwhichweexposethepairsofthe randommatching. nishthissectionandthenskipaheadtothelastone. readerwhoisnotinterestedinthedetailsoftheproofmightwanttojust chromaticnumberofsparserandomgraphs,andanewproofofaclassical double-jumptheorem,showingthatthisworkgeneralizesthatresult.a whichhavedegreeiasfollows: exposed.avertexsomebutnotallofwhosecopiesareinexposedpairsis GivenD,wewillexposearandomcongurationFonnvertices,di(n)of Inordertoexaminethecomponentsofourrandomconguration,wewill partiallyexposed.allotherverticesareunexposed.thecopiesofpartially exposedverticeswhicharenotinexposedpairsareopen. 1.FormasetLconsistingofidistinctcopiesofeachofthedi(n)vertices Ateachstep,avertexallofwhosecopiesareinexposedpairsisentirely 2.RepeatuntilLisempty: whichhavedegreei. (a)exposeapairoffbyrstchoosinganymemberofl,andthen Allrandomchoicesaremadeuniformly. (b)repeatuntiltherearenopartiallyexposedvertices: Chooseanopencopyofapartiallyexposedvertex,andpairit withanotherrandomlychosenmemberofl.removethemboth choosingitspartneratrandom.removethemfroml. atime.whenanycomponentiscompletelyexposed,wemoveontoanew one;i.e.werepeatstep2(a). Essentiallyweareexposingtherandomcongurationonecomponentat ofthisfreedom,butinmostcaseswewillpickitrandomlyinthesamemanner 2(a).Inafewplacesinthispaper,itwillbeimportantthatwetakeadvantage withthesameprobabilityunderthisprocedure,andhencethisisavalidway inwhichwepickalltheothervertex-copies,i.e.unlesswestateotherwise, tochoosearandomconguration. Itisclearthateverypossiblematchingamongstthevertex-copiesoccurs wewillalwaysjustpickauniformlyrandommemberofl. NotethatwehavecompletefreedomastowhichvertexwepickinStep 8

isexposed.iftheneighbourofvchoseninstep2(b)isofdegreed,then Xigoesupbyd?2.Eachtimeacomponentiscompletelyexposedand werepeatstep2(a)thenifthepairexposedinstep2(a)involvesverticesof ofdegreedisrd,thentheprobabilitythatwepickacopyofavertexof degreed1andd2thenxiissettoavalueofd1+d2?2. Now,letXirepresentthenumberofopenvertex-copiesaftertheithpair degreedinstep3(b)isrd=pi1ri.thereforeinitiallytheexpectedchange inxiisapproximatelypi1i(i?2)di(n) Notethatifthenumberofvertex-copiesinLwhicharecopiesofvertices Walk",withanexpectedchangeofQ(D) Markovprocessverycloseindistributiontothewell-studied\Drunkard's standardresultofrandomwalktheory(seeforexample[9])impliesthatif Therefore,atleastinitially,ifthisvalueispositivethenXifollowsa Pj1jdj(n)=Q(D) Q(D)>0,thenafter(n)steps,Xiisa.s.oforder(n). K.SinceXi+1Xi?1always,a K: quickly,andthiswillgiveustheotherpartoftheorem1,asthesizesof seethatifq(d)isbounded,thenthisgiantcomponentisa.s.unique. thecomponentsoffareboundedabovebythedistancesbetweenvaluesof cyclesinit,andthiswillgiveustherstpartoftheorem1.wewillalso on(n)vertices.wewillseethatsuchacomponenta.s.hasatleast(n) Ontheotherhand,ifQ(D)<0,thenXia.s.returnstozerofairly Itfollowsthatourrandomcongurationa.s.hasatleastonecomponent isuchthatxi=0. Therearethreemajorcomplications: 1.Apurerandomwalkcandropbelow0.WheneverXireaches0,it OfcoursetherandomwalkfollowedbyXiisnotreallyassimpleasthis. 3.Asmoreandmoreverticesareexposed,theratioofthemembersofL 2.Weneglectedtoconsiderthatthesecondvertex-copychoseninStep Wewillcallsuchapairofvertex-copiesabackedge. resetsitselftoapositivenumber. ofxichanges. whicharecopiesofverticesofdegreedshifts,andtheexpectedincrease 2(b)mightbeanopenvertex-copyinwhichcaseXidecreasesby2. 9

1.ThiswillincreasetheprobabilityofXigrowinglarge,andsothisonly Thesecomplicationsarehandledasfollows: posesapotentialprobleminprovingpart(b).inthiscase,wewill 3.Inprovingpart(a),welookatourcomponentatatimewhentheexpectedincreaseinXiisstillatleast12itsoriginalvalue.Wewillsee thatthecomponentbeingexposedatthispointisa.s.agiantcomponent.inprovingpart(b),itisenoughtoconsidertheconguration 2.Wewillseethatthisa.s.doesn'thappenoftenenoughtoposeaserious problem,unlessthepartiallyexposedcomponentisalreadyofsize(n). o(n?1),andhenceevenifwe\tryagain"ntimes,thiswilla.s.never happen. showthattheprobabilityofacomponentgrowingtoobigisoforder 2GraphsWithNoLargeComponents twosections. Thisisaroughoutlineoftheproof.Wewillllinthedetailsinthenext signicantly. aftero(n)steps.atthispoint,theexpectedincreasehasn'tchanged theconditionsgivenintheorem1(b),thenfa.s.doesnothaveanylarge graphs.wewillrstprovethatiffisarandomcongurationmeeting components. InthissectionwewillprovethattheanalogueofTheorem1(b)holdsfor randomcongurations.lemma1willthenimplythatitholdsforrandom somefunction0!(n)n18?fhasnoverticesofdegreegreaterthan!(n),thenfa.s.hasnocomponentswithmorethan=dr!(n)2logne vertices. sequencednmeetingtheconditionsoftheorem1.ifq(d)<0andiffor GivenQ(D)<0set=?Q(D)=KandsetR=150 Lemma3LetFbearandomcongurationwithnverticesanddegree ThefollowingtheoremofAzumawillplayanimportantrole: 2. Azuma'sInequality[1]Let0=X0;:::;Xnbeamartingalewith jxi+1?xij1 10

forall0i<n.let>0bearbitrary.then eachi:maxje(f()j1;2;:::i+1)?e(f()j1;2;:::i)jci f()=f(1;2;:::;n)bearandomvariabledenedbythesei.iffor Thisyieldsthefollowingveryusefulstandardcorollary. CorollaryLet=1;2;:::;nbeasequenceofrandomevents.Let Pr[jXnj>pn]<e?2=2 jf?e(f)j>tisatmost:2exp wheree(f)denotestheexpectedvalueoff,thentheprobabilitythat argumentsseeeither[16]or[5]. Formoredetailsonthiscorollaryandanexcellentdiscussionofmartingale 2Pc2i! insection1. InordertoproveLemma3,wewillanalyzetheMarkovprocessdescribed?t2 sumofdeg(v)?2overallverticesvcompletelyorpartiallyexposedduring therstisteps.wenotethatwi=xi+2yi?2ci. backedgesformed,andcibethenumberofcomponentsthathavebeenat leastpartiallyexposedduringtherstisteps.wealsodenewitobethe congurationhavebeenexposed.similarly,weletyibethenumberof NowWi\stalls"wheneverabackedgeisformed,andonlychangeswheneveranewvertexiscompletelyorpartiallyexposed.Forthisreason,itis easiertoanalyzewiwhenitisindexednotbythenumberofpairsexposed, rstjnewvertices(partiallyorcompletely)exposed. ablewhichdoesexactlythis.weletzjbethesumofdeg(v)?2overthe butbythenumberofnewverticesexposed.thusweintroduceanothervari- expectedincreaseasxi,butbehavesmuchmorenicely.inparticular,it Section1.Specically,ifaftertherstjverticeshavebeencompletelyor isn'taectedbytherstandsecondcomplicationsdiscussedattheendof ThereasonthatweareintroducingZjisthatithasthesameinitial RecallthatXiisthenumberofopenvertex-copiesafteripairsofour 11

partiallyexposed,thereareexactlyri(j)unexposedverticesofdegreei,then Zj+1=Zj+(i?2)withprobabilityiri(j)=Piri(j). thann?2. thejthvertexispartiallyexposed;i.e.wij=zj. vinf,theprobabilitythatvliesonacomponentofsizeatleastisless randomvariableijwhichisthenumberofpairsexposedbythetimethat Recallthat=R!(n)2logn. Lemma4SupposethatFisasdescribedinLemma3.Givenanyvertex NowinordertodiscussXiandZjatthesametime,wewillintroducethe thatxi>0forall1i.thus,wewillconsidertheprobabilityofthe latter. canalsogetzixi?2.thisisbecauseateachiterationweeitherhavea theprobabilitythatvliesonacomponentthatlargeisatmosttheprobability Notethatforanyi,ifCi=1thenWi=Xi+2Yi?2Xi?2.Infact,we Proof: backedgeorexposeanewvertex.thusiniterationi,wehaveexposedi?yi HerewewillinsistthatvistherstvertexchoseninStep2(a).Therefore step;thereforezizi?yi?yiwi?yixi+yi?2xi?2. newvertices,thereforewi=zi?yi.nowzidecreasesbyatmostoneateach thatxi>0forall1iisatmosttheprobabilitythatz>?2.we willconcentrateonthisprobability,aszibehavesmuchmorepredictably thanxi. NowifXi>0forall1i,thenC=1.Therefore,theprobability rstjvertex-copieschosenwereallcopiesofverticesofdegree1.ifthiswere thecasethentheexpectedincreaseinzjwouldbe:?2ṫhisistruebecausetheexpectedincreaseofzjwouldbehighestifthe?+o(1).weclaimthatforj,theexpectedincreaseinzjislessthan InitiallytheexpectedincreaseinZjisPi1i(i?2)di(n)=Pi1idi(n)= forsucientlylargen,asj=o(n)andidi(n)!iuniformly. Therefore,theexpectedvalueofZislessthan?2+deg(v)<?3.?(d1(n)?j)+Pi2i(i?2)di(n) (d1(n)?j)+pi2idi(n)+o(1)=?+o(1) 12?2

veryclosetoitsexpectedvalue. andf()=z.weneedtobound WewillusethecorollaryofAzuma'sInequalitytoshowthatZisa.s. Letbethesetofunexposedverticesatthispoint.Thesizeofisn?i. iwillindicatethechoiceoftheithnewvertexexposed,i=1;:::;, istheeventthatxisthe(i+1)stnewvertexexposed. Supposethatwearechoosingthe(i+1)stvertextobepartiallyexposed. Foreachx2,deneEi+1(x)tobeE(Zj1;2;:::;i+1)wherei+1 Consideranytwoverticesu;v2.WewillboundjEi+1(u)?Ei+1(v)j. je(f()j1;2;:::i+1)?e(f()j1;2;:::i)j: nextvertex.now,z=zj?1+(px2sdeg(x)?2)+deg(y1)?2+deg(y2)?2, thedistributionofthisorderisunaectedbythepositionsofu;v. w.therefore,themostthatchoosingbetweenu;vcanaecttheconditional wherey1isthejthvertexexposed(eitheruorv)andy2iseitheru;v;or expectedvalueofzistwicethemaximumdegree,i.e.jei+1(u)?ei+1(v)j Considertheorderthattheverticesin?fu;vgareexposed.Notethat 2!(n). LetSbethesetoftherst?2verticesunderthisorder,andletwbethe wehavethat Since,E(f()j1;2;:::i)=Xx2PrfxischosengEi+1(x); Z>0isatmost:2exp ThereforebythecorollaryofAzuma'sInequality,theprobabilitythat je(f()j1;2;:::i+1)?e(f()j1;2;:::i)j2!(n): AndnowLemma3followsquiteeasily: ProofofLemma3:?(3R!(n)2logn)2 2P(2!(n))2!=2n?2 <n?2: 72R 13 2

ofsizeatleastiso(1).thereforea.s.noneexist. throughouttheexposureofourconguration. ByLemma4,theexpectednumberofverticeswhichlieoncomponents wouldnotbeabletoreach0withinr!(n)2lognsteps. WealsogetthefollowingCorollary: Corollary3UnderthesameconditionsasLemma3,a.s.Xi<2 Proof: BecauseXidropsbyatmost2ateachstep,ifitevergotthathigh,it seethatita.s.hasnomulticycliccomponents. asinlemma3.fa.s.hasnocomponentwithatleast2cycles. WewillnowshowthatFa.s.doesn'thavemanycycles.First,wewill Lemma5LetFbearandomcongurationmeetingthesameconditions Proof: Chooseanyvertexv.LetEvbetheeventthatvliesonacomponentof 2 ofthiscomponent,xi<2. sizeoftherstcomponentisatmostthenthesecondbackedgemustbe sizeatmostwithmorethanonecycle,andthatthroughouttheexposure chosenwithinatmost+2steps.thereforetheprobabilitythateholdsis lessthan WewillinsistthatvistherstvertexexaminedunderStep2(a).Ifthe sotheprobabilitythatevholdsforanyviso(1). as!(n)<n18?. ThereforebyLemma3andCorollary3a.s.nocomponentsofFhave ThereforetheexpectednumberofverticesforwhichEvholdsiso(1)and 2!2 M?2?32=on?1 morethanonecycle. asinlemma3.fa.s.haslessthan2logncycles. thatita.s.doesnothavemanycycliccomponents. WecannowshowthatFa.s.doesnothavemanycycles,byshowing Lemma6LetFbearandomcongurationmeetingthesameconditions Proof: Wewillshowthata.s.throughouttheexposureofF,atmost2logn 2 back-edgesareformed.therestwillthenfollow,sincebylemma5,a.s.no infisexactlythenumberofbackedges. componentcontainsmorethanonecycle,andsoa.s.thenunmberofcycles 14

i.clearlythenumberofbackedgesformedisatmostthenumberofsuccessfulti's,plusthenumberofbackedgesformedattimeswhenxi>2. NowbyCorollary3,weknowthattherearea.s.noneofthelattertypeof M?2i+12and1otherwise. Nowthenumberofvertex-copiestochoosefromisPj1jdj(n)?2i+ ThereforetheexpectedvalueofT,thenumberofsuccessfulTi'sis: E(T)=2+(M?2)=2 Xi=1M?2i+1=log(M)(1+o(1)) 2 M?2i+1,for addallofltobi.lettibetheeventthatamemberofbiischoseninstep tion,thenletbiconsistofany2ofthem.otherwise,letbiconsistofthe sizeofbiupto.ofcourseiflistoosmalltodothis,thenwewilljust openvertex-copiesandenougharbitrarilychosenmembersofltobringthe FirstwemustdeneasetBiofunmatchedvertex-copies: Foreachi,iftherearemorethan2openvertex-copiesattheithitera- 1=M?2i+1.ThereforetheprobabilityofTiholdingis2 backedges,sowewillconcentrateonthenumberoftheformertype. muchbiggerthane(t). NowwewilluseasecondmomentargumenttoshowthatTisa.s.not Therefore,byChebyshev'sinequality,theprobabilitythatT>1:5log(M) E(T2)=Xi6=j =(E(T)2+E(T))(1+o(1)) (M?2i+1)(M?2j+1)+E(T) 2 2logn,provingtheresult. isatmost1=(4e(t))(1+o(1))=o(1). ProofofTheorem1(b): ThisclearlyfollowsfromLemmas2,3,5,and6. AndnowwecanproveTheorem1(b). Therefore,a.s.thenumberofbackedgesformedislessthan1:5log(M)< 2 15

3GraphsWithGiantComponents InthissectionwewillprovetheanalogueofTheorem1(a)forrandomcon- gurations.lemmas1and2willthenimplythattheorem1(a)holds. existconstants1;2>0dependentondsuchthatfa.s.hasacomponent sequencednmeetingtheconditionsoftheorem1.ifq(d)>0thenthere withatleast1nverticesand2ncycles.moreover,theprobabilityofthe converseisatmostzn,forsomexed0<z<1. Lemma7LetFbearandomcongurationwithnverticesanddegree Firstwewillshowthatagiantcomponentexistswithhighprobability: 0<<a.s.Zdne>n.Moreover,theprobabilityoftheconverseisat hold. discussedintheprevioussection.again,thekeywillbetoconcentrateon therandomvariablezj. AsinSection2,wewillproveLemma7byanalyzingtheMarkovprocess ThroughoutthissectionwewillassumethattheconditionsofLemma7 partnerispi(n)=idi(n)=pj1jdj(n)=ii=k+o(1). most(z1)n,forsomexed0<z1<1. Proof: Forsimplicity,wewillassumethatnisaninteger. Initially,theprobabilitythatavertex-copyofdegreeiischosenasa Lemma8Thereexists0<<1;0<<min(14;K4)suchthatforall attheendofsection2,i.e.thatfactthattheratiosofunexposedverticesof dierentdegreesareshifting. (n)steps.thuswehavetoworryaboutthethirdcomplicationdescribed UnlikeinSection2,wehavetoconsiderthebehaviourofourwalkafter i>iischosen,wesubtractonefromzjinsteadofaddingi?2toit,then thatifwechangezjslightlybysayingthateverytimeavertexofdegree ofhighdegree.sowhatwewilldoisshowthatwecanndavaluei,such wewillstillhavepositiveexpectedincrease. one,suchthatforeach2ii,iisalittlelessthantheinitialprobability Wewillthenshowthatwecanndasequence1;:::;isummingto Itturnsoutthatthisproblemismuchlessseriousifwecanignorevertices wewouldstillhaveapositiveexpectedincrease. ofavertexofdegreeibeingchosen.however,ifweweretoadjustzjalittle furtherbyselectingavertexofdegreeiwithprobabilityiateachstep,then Wewillcallthis\adjustedZj"Zj.Clearly,ifwendsomeJsuchthat 16

asbigastheprobabilitythatzj>r.wewillconcentrateonthesecond afterjsteps,theprobabilityofchoosingavertexofdegreeiisstillatleast ifor2ii,thentheprobabilitythatzj>rforanyrisatleast probabilityaszjismuchsimplertoanalyze. 1.Pi=1; 2.0<i<ii=K,for2ii,unless0=i=ii=K; 3.Pi1i(i?2)i>0. Moreformally,whatwewishdoischooseasequence1:::;isuchthat Notethat:Xi1(i?2)pi(n)=Xi1(i?1)pi(n)?Xi1pi(n) ii,pi>i>0unlesspi=i=0,1=1?2?3?:::?i,and PiSinceDiswell-behavedandQ(D)>0,thereexistsisuchthat i=2(i?1)pi>1+0,forsome0>0andsucientlylargen. Therefore,wecanchooseasequence1;:::;isuchthatforall2 Setpi=ii=K. =Xi2(i?1)pi(n)?1: walk: i=2(i?1)i=1+0 ConsidertherandomvariableZjwhichfollowsthefollowingrandom Zj+1=Zj+(i?2)withprobabilityi,1ii. Z0=0 2.ItfollowsthatPi1(i?2)i=0 Fori=2;:::;i,chooseanyi>0suchthatii?i 2. =minf2;:::;i;k4g.clearly,afteratmostiterations,theprobability ofchoosingacopyofavertexofdegreei2isatleasti.therefore,for 0jn,therandomvariableZjmajorisesZj;i.e.foranyR: NowtheexpectedincreaseinZjatanystepis0 Pr[Zj>R]Pr[Zj>R:] 2.Thusthelemma K<i,andset followsbyletting=04,asitiswell-known(seeforexample[9])thatzn 17

asclaimed. isa.s.concentratedarounditsexpectedvaluewhichis2n,andthatthe probabilityofdeviatingfromtheexpectedvaluebymorethan(n)isaslow analyzexj.inordertodothis,recallthattherandomvariableijisdened tobethenumberofpairsexposedbythetimethatthejthvertexispartially exposed;i.e.wij=zj. a.s.existssome1iidne.suchthatxi>n,where=min(2;14). WehavejustshownthatZja.s.growslarge.However,wereallywantto Lemma9Thereexists0<0<suchthatforany0<0there 2 Moreover,theprobabilityoftheconverseisatmost(z2)nforsome0<z2<1, dependenton. chosenisxi a.s.w<2nfor0. orinpairshavebeenexposed.weclaimthatwecanchoose0suchthat Proof: Forsimplicity,wewillassumethatnisaninteger. Atanystepi;1iIn,theprobabilitythatanopenvertex-copyis WewillcountW,thenumberofbackedgesformedbeforeeitherXi>n 0ifXj>nandatmostp= NowIjj+YIjj+Zj Therefore,ateachstep,theprobabilitythatsuchabackedgeisformedis Kn?2i+o(1),regardlessofthechoicesmadeprevioustothatstep. ifxjn. 2(+)n. variablebin(p;in). Thusthenumberofsuchcopieschosenismajorisedbythebinomial ThereforethelemmafollowssolongaspInp(+)n<2n,whichis K?2?2 12 thatthereisa.s.agiantcomponent: equivalentto4+4<k,yielding0. atstepi=id0newilla.s.haveatleast1nverticesand2ncycles.moreover, atleast NowifXinforall1i<InthenWisequaltoYIn. theprobabilityoftheconverseisatmost(z3)n,forsomexed0<z3<1. NowthatweknowthatXia.s.getstobeaslargeas(n),wecanshow Lemma10Thereexists1;2>0suchthatthecomponentbeingexposed ThereforeXIn=Zn?2YInwhichwithprobabilityatleast1?(z1)nis 2n,whichyieldsourresult. 2 18

1nofthesewillbematchedwithmembersof,andatleast2nofthesewill beforethiscomponentisentirelyexposed.wewillshowthata.s.atleast vertices.formasetconsistingofexactlyonecopyofeachofthem. Proof: lemma. bematchedwithotheropenvertex-copiesfrom.clearlythiswillprovethe Notethatatthispointtherearea.s.atleastn?20n?n>n5unexposed ThereisasetofXIopenvertex-copieswhosepartnersmustbeexposed whereeachmatchingisequallyprobable.theexpectednumberofpairs containingonevertexfromeachof;isatleastn5xi cedureforexposingfsimplygeneratesarandommatchingamongstthem NowthereareM?2Ivertex-copiesavailabletobematched.Ourprobers,anditfollowsfromtheChernoboundsthatthesenumbersarea.s.at asclaimed.thereforethecomponenta.s.hasatleast1nverticesandat leasthalfoftheirexpectedvalueswiththeprobabilityoftheconverseaslow pectednumberofpairsofopenvertex-copieswhichformanedgeoffis M2?IXI Thepreviouslemmasgiveusalowerboundof21n;22nonthesenum- M?2I2. 2M?I,andtheex- least2ncycles. thenfa.s.hasexactlyonecomponentonmorethantlognvertices,for AndnowLemma7followsquiteeasily: ProofofLemma7: ThisisclearlyacorollaryofLemma10. WewillnowseethatFa.s.hasonlyonelargecomponent. Lemma11IfFisarandomcongurationasdescribedinLemma7, 2 ertyaifuandvlieoncomponentsofsizeatleast1nandtlognrespectively. WewillshowthatforanappropriatechoiceofT,theprobabilitythat(u;v) sizeatleast1n.wewillseeherethatnoothercomponentsoffarelarge. someconstanttdependentonthedegreesequence. haspropertyaiso(n?2),whichisenoughtoprovethelemma. Consideranyorderedpairofvertices(u;v).Wesaythat(u;v)hasprop- Proof: Recallthatwemaychooseanyvertex-copywewishtostarttheexposure WehavealreadyshownthatFa.s.hasatleastonegiantcomponentof with.wewillchooseu. ByLemma9,therea.s.existssomeIId1ne,suchthatXI>nwhere 19

thecontrary.denetobethesetofopenvertex-copiesafteristeps. exposedacopyofv,then(u;v)doesnothavepropertya,sowewillassume afteristeps,wearenotstillexposingtherstcomponent,c1,orifwehave =min(0 restofc1untillater.wewillseethatifc2getstoobig,thenitwilla.s. exposingv'scomponent,c2,immediately,andputotheexposureofthe Herewewillbreakfromthestandardmethodofexposure.Wewillstart 2;1 2;14),andsowecanassumethistobethecase.Notethatif v,andexposingitspartner.wecontinueexposingpairs,alwayschoosing includeamemberof. acopyofapartially-exposedvertexwhichisknowntobeinc2(ifoneis available),andexposingitspartner.wechecktoseeifthispartnerliesin.thiswouldimplythatvliesinc1.oncec2isentirelyexposed,ifitis disjointfromc1thenwereturntoexposingtherestofc1andcontinueto exposefinthenormalmanner.notethatthisisavalidwaytoexposef. WeexposeC2inthefollowingway.Westartbypickinganycopyof atleasttlognstepstoexposethiscomponent.therefore,theprobability thatvliesonacomponentofsizegreaterthantlognwhichisnotc1isat Also,ifvliesonacomponentofsizegreaterthanTlogn,thenitmusttake most: Ateachstep,theprobabilitythatamemberofischosenisatleastK. tozeroasn!1,soa.s.noneexist. forasuitablevalueoft. ThereforetheexpectednumberofpairsofverticeswithpropertyAtends 1?KTlogn=on?2 morethanonecycle. thenfa.s.hasnomulti-cycliccomponentonatmosttlognvertices,for anyconstantt. ItonlyremainstobeshownthatFa.s.hasnosmallcomponentswith Lemma12IfFisarandomcongurationasdescribedinLemma7, Proof: 2 ofstep2(a).nowifthiscomponentcontainsatmosttlognvertices,then itisentirelyexposedafteratmosto(n1=4)stepsasthemaximumdegreeis n1=4?. Considertheprobabilityofsomevertexvlyingonsuchacomponent. Wewillinsistthatweexposeanedgecontainingvintherstexecution 20