微博复杂网络适应度模型的研究

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1、ABSTRACJ^TTHEREARCHOFFITNESSNETWORKMODEL0NWEIB0COMPLEXNEllv、，ORKABSTRACTInrecentyears，withtherapiddevelopmentofInternettechnology,alargenumberofonlinesocialnetworkshavesprungup，suchasFackbook，Twitter,YouTubeanddomesticRenRen，SinaWeibo，etc．Weibo，asanewtypeofonlinesocialnetwork，mapsande

2、xtendsrealsocialrelationshipofpeopletoonlinenetwork．Itreflectspeople’sconnectionrelationshipinonlinenetwork．Complexnetworkresearchershavealreadyfocusedonthestructureandevolutionofonlinesocialnetwork．IthasimportanttheoreticalandpracticalsignificancetounderstandingWeibotopologystructure

3、，whichwillhelpUStounderstandtheinformationtransmissioni11thenetwork．Complexnetworktheoryhasbeenprovedtobeastrongmethodofunderstandingcomplexsystemstructureanddynamiccharacteristics．Inthispaper,wehaveresearchedonSinaWeibo．BasedonSinaWeibonetworkstructureandcharacteristics，wehavemodeled

4、therelationshipnetworkofSinaWeibousersbyclassicalcomplexnetworktheoryandmodels．SinceithassomedefectstoapplyFitnessnetworkmodeltoWeibousersrelationshipnetwork，wehaveimprovedFitnessnetwork北京化工大学硕士学位论文modelwithPricenetworkmodelandgottenP-Fitnessnetworkmodel．Furthermore，aimingatthedeficie

5、ncyinP-Fitnessnetworkmodel，wefinallyproposedSP—FitnessnetworkmodelafterimprovingP-Fitnessnetworkmodel。Atthesametime，throughtheresearchofrealWeibousersonthedistributionsoffans，UserPR(PageRank)valuesandactivities，wehaveanalyzedWeibousersinfluence，topinfluenceusersandthecharacteristicsof

6、theirweibos．Wehaveproposednode—fitnessalgorithmforSP—FitnessnetworkmodelbytakingthethreefactorsofuseFsinfluence，includingfans，UserPRandactivitiesintoconsideration．SP．FitnessnetworkmodelreflectsthegrowthmechanismandpreferentialconnectioncharacteristicsofrealWeibousersrelationshipnetwor

7、k．Italsoreflectsthatuserswithhigherfitness(suchascelebrities)maygetmoreconnectionsthanoldernormalusersandbecomehubnodeseventheyjointhenetworklater．SP—FitnessmodelcanbeappliedtoWeibousersrelationshipnetworkanditwillprovidethestructurefoundationforfurtherresearchingoninformationdissemin

8、ation