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.,1,EvolutionaryModelsandDynamicalPropertiesofComplexNetworks,Name:JianguoLiuUniversityofShanghaiforScienceandTechnology2010-3-24,.,2,Outline,ComplexnetworksanalysisbyCitespaceNetworkevolutionmodelsDynamicalpropertiesonscale-freenetworksPersonalizedrecommendation,.,3,1999年-2010年发表的以“complexnetworks”为主题词的SCI论文数,.,4,Citespace软件介绍,CiteSpace：由美国德雷赛尔大学信息科学与技术学院的陈超美开发。该程序可以登录到/cchen/citespace后免费使用。利用Citespace寻找某一学科领域的研究进展和当前的研究前沿，及其对应的基础知识。,.,5,复杂网络论文作者合作网(1999-2010),.,6,复杂网络研究小组状况(1999-2010),.,7,复杂网络各个国家研究状况(1999-2010),.,8,利用引文分析观察当前的研究热点(1999-2010),.,9,Topcitedauthors(1999-2010),.,10,各研究领域之间的关系(1999-2010),.,11,个性化推荐的知识图谱,.,12,Topcitedauthors,.,13,目前的研究热点,.,14,Outline,BackgroundintroductionNetworkevolutionmodelsDynamicalpropertiesonscale-freenetworksPersonalizedrecommendation,.,15,2.Scale-freeNetworkEvolutionModels,Multistagerandomgrowingsmall-worldnetworkswithpower-lawdegreedistributionGrowingscale-freenetworkmodelwithtunableassortativecoefficientSelf-learningmutualselectionmodelforweightednetworksRandomevolvingnetworksunderthediameteranddverageconnectivityconstraint,.,16,2.1.Multistagerandomgrowingsmall-Worldnetworkswithpower-lawdegreedistribution,LiuJian-Guo,DangYan-ZhongandWangZhong-Tuo,ChinesePhysicsLetters23(3)746-749(2006),Onenodeisaddedineachtimestep；Selectthenodeuaccordingtothepreferentialmechanism；Selectaneighbornodeofnodeu；,.,17,.,18,Onenodeisaddedineachtimestep；Selectthenodeuaccordingtothepreferentialmechanism；Selectaneighbornodeofnodeuaccordingtops；,2.2.Growingscale-freenetworkmodelwithtunableassortativecoefficient,QiangGuo,TaoZhou,Jian-GuoLiuetal.,PhysicaA371814-822(2006),.,19,.,20,Twoparameters:attractivefactorp,thenumberofcandidatesm,2.3Self-learningmutualselectionmodelforweightednetworks,Jian-GuoLiuetal.,DCDISBSupplement,ComplexNetworks,14(S7)33-36,(2007).,1,2,3,4,1,2,3,4,5,m=2,.,21,.,22,2.4RandomEvolvingNetworksUndertheDiameterandAverageConnectivityConstraint,Thegrowthofrandomnetworksundertheconstraintthatthediameter,definedastheaverageshortestpathlengthbetweenallnodes,andtheaverageconnectivityremainsapproximatelyconstantisstudied.Weshowedthat,ifthenetworkmaintainstheformofitsdegreedistributionandthemaximaldegreeisaN-dependentcutofffunction,thenthedegreedistributionwouldbeapproximatelypower-lawwithanexponentbetween2and3.,Jian-GuoLiuetal.,JournalofSystemScienceandSystemEngineering16(1)107-112(2007).,.,23,Motivation,Inthebiologicalnetworks,theconstantdiametermayberelatedtoimportantpropertiesofthesebiologicalnetworks,suchasthespreadandspeedofresponsestoperturbations.IntheInternetbackbonenetwork,theaveragedistanceisoneofthemostimportantfactorstomeasuretheefficiencyofcommunicationnetwork,anditplaysasignificantroleinmeasuringthetransmissiondelay.Theseconstraintscanbethoughtofastheenvironmentalpressures,whichwouldselecthighlyefficientstructuretoconveythepacketsinit.,.,24,Motivation,.,25,Constructionofthemodel,TheexpressionforthediameterdofarandomnetworkwitharbitrarydegreedistributionwasdevelopedWhereistheaveragedegree,.,26,InordertoseekadegreedistributionthatmaintainsitsdistributionandhasanapproximatelyconstantdiameterindependentofN.TheparameterNcanbeaccomplishedbyimposingaN-dependentcutofffunction,.,27,Thedistributionp(k)canbedeterminedbywritingthisequationforandAlgebraicmanipulationyieldstherelation,.,28,Usinganintegralapproximation,amoreexplicitformulationcanbewrittenasfollowing.,.,29,Whenthenumericallycalculateddegreedistributionsforvariousvaluesof,.,30,Discussionofparttwo,Wehavepresentedareasonfortheexistenceofpower-lawdegreedistributionunderthediameterconstraintobservedintheInternetbackbonenetworkwherethereareevolutionarypressurestomaintainitsdiameter.Ouranalysisshowsthat,ifthemaximaldegreeisaN-dependentcutofffunction,theformofarobustnetworkdegreedistributionshouldbepowerlawtomaintainitsdiameter,whiletheaverageconnectivitypernodeaffectthedistributionexponentslightly.,.,31,Outline,BackgroundintroductionNetworkevolutionmodelsDynamicalpropertiesoncomplexnetworksPersonalizedrecommendation,.,32,3.1Structuraleffectsonsynchronizabilityofscale-freenetworks,.,33,3.1Howtomeasurethesynchronizability,WhereQistheratiooftheeigenvalues.ThesynchronizabilitywouldbeincreasedasQdecreases,viceverse.,.,34,Theedgeexchangemethodisintroducedtoadjustthenetworkstructure,andthetabusearchalgorithmisusedtominimizetheeigenvalueratioQ,min,QiangGuo,LiuJian-Guo,etal,ChinesePhysicsLetters24(8)(2007)2437-2440.,.,35,Insummary,usingthetabuoptimalalgorithm,wehaveoptimizednetworksynchronizabilitybychangingtheconnectionpatternbetweendifferentpairsofnodeswhilekeepingthedegreedistribution.Startingfromscale-freenetworks,wehavestudiedthedependencebetweenthestructuralcharacteristicsandsynchronizability.Thenumericalresultssuggestthatascale-freenetworkwithshorterpathlength,lowerdegreeofclustering,anddisassortivepatterncanbeeasilysynchronized.,.,36,3.1Structuraleffectsonsynchronizability,min,max,Combiningthetabusearch(TS)algorithmandtheedgeexchangemethod,weenhanceandweakenthesynchronizabilityofscale-freenetworkswithdegreesequencefixedtofindthestructuraleffectsofthescale-freenetworkonsynchronizability,LiuJian-Guo,etal,InternationalJournalofModernPhysicsC18(7)1087-1094(2008).,.,37,ThenumericalresultsindicatethatD,C,randBminfluencesynchronizabilitysimultaneously.Especially,thesynchronizabilityismostsensitivetoBm.,.,38,Effectoftheloopstructureonsynchronizability,.,39,Outline,BackgroundintroductionNetworkevolutionmodelsDynamicalpropertiesoncomplexnetworksPersonalizedrecommendation,.,40,Personalizedrecommendation,Improvedcollaborativefilteringalgorithmbasedoninformationtransaction.Ultraaccuracyrecommendationalgorithmbyconsideringthehigh-orderusersimilaritiesEffectofusertastesonpersonalizedrecommendation,.,41,Whyrecommend,Wefacetoomuchdataandsourcestobeabletofindoutthosemostrelevantforus.Indeed,wehavetomakechoicesfromthousandsofmovies,millionsofbooks,billionsofwebpages,andsoon.Evaluatingallthesealternativesbyourselvesisnotfeasibleatall.,Asaconsequence,anurgentproblemishowtoautomaticallyfindouttherelevantobjectsforus.,.,42,Collaborativefilteringalgorithm,Herlockeretal.,ACMTrans.Inf.Syst.22:5-53(2004),.,43,Content-basedalgorithm,Theuserwillberecommendeditemssimilartoth