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基于共同好友数的在线社会网络社区发现算法.docx

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基于共同好友数的在线社会网络社区发现算法 Title:CommunityDetectionAlgorithmBasedonMutualFriendCountsinOnlineSocialNetworks Abstract: Therapidgrowthandpopularityofonlinesocialnetworkshaverevolutionizedthewaypeoplecommunicateandinteractwitheachother.Asthesenetworkscontinuetoexpand,theneedtodiscoverandanalyzecommunitieswithinthembecomesincreasinglyimportant.Thispaperpresentsacommunitydetectionalgorithmthatisbasedonthemutualfriendcountsinonlinesocialnetworks.Theproposedalgorithmaimstouncovermeaningfulcommunitiesbyleveragingtherelationshipsamongusersandtheircommonfriendships. 1.Introduction 1.1Background 1.2Motivation 1.3Objective 2.RelatedWork 2.1TraditionalCommunityDetectionAlgorithms 2.2CommunityDetectioninOnlineSocialNetworks 2.3LimitationsofExistingApproaches 3.Methodology 3.1DataCollectionandPreprocessing 3.2MutualFriendCountCalculation 3.3CommunityDetectionAlgorithm 3.4AlgorithmEvaluation 4.ExperimentalResults 4.1DatasetDescription 4.2EvaluationMetrics 4.3PerformanceComparison 4.4AnalysisofResults 5.Discussion 5.1AdvantagesandLimitations 5.2ImplicationsandPotentialApplications 5.3FutureResearchDirections 6.Conclusion 1.Introduction 1.1Background: Theadventofonlinesocialnetworkshasdramaticallytransformedthewaypeopleconnectandinteractwitheachother.Theseplatformshavebecomeanintegralpartofmodernsociety,offeringvastopportunitiesforcommunication,contentsharing,andcollaboration.Withmillionsofusersactivelyparticipating,onlinesocialnetworkshavegeneratedmassiveamountsofdata,makingitchallengingtoeffectivelyidentifyandunderstandtheunderlyingstructuresandcommunitieswithinthem. 1.2Motivation: Communitydetection,orclustering,inonlinesocialnetworksiscrucialforvariousapplications,includingrecommendationsystems,advertisingtargeting,andsentimentanalysis.However,identifyingmeaningfulcommunitiesinlarge-scalenetworkspresentssignificantcomputationalchallenges.Existingalgorithmsmaystrugglewiththescalabilityandaccuracyrequiredtohandletheever-increasingsizeandcomplexityofsocialnetworkdata. 1.3Objective: Themai