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基于动态网络表示的链接预测.docx

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基于动态网络表示的链接预测 Title:LinkPredictionBasedonDynamicNetworkRepresentation Abstract: Linkpredictionisanessentialtaskinnetworkanalysisthataimstopredictmissingorfutureconnectionsinanetwork.Traditionallinkpredictionmethodsoftenrelyonstaticnetworkrepresentationsandignorethedynamicnatureofnetworks.However,manyreal-worldnetworksaredynamic,constantlyevolving,andchangingovertime.Dynamicnetworkrepresentationmethodscapturethetemporaldynamicsandchangingpatternsofnetworks,providingamoreaccurateandeffectiveapproachforlinkprediction.Thispaperexplorestheconceptofdynamicnetworkrepresentationsandpresentsanoverviewofvariousapproachesandtechniquesusedforlinkpredictionbasedondynamicnetworkrepresentation. 1.Introduction: Therapiddevelopmentofnetworkanalysishasledtogrowinginterestinlinkprediction,asithasnumerousapplicationsinvariousdomainssuchassocialnetworks,biologicalnetworks,transportationnetworks,andrecommendationsystems.Linkpredictionmethodsaimtoestimatethelikelihoodofalinkoredgebetweentwonodesinanetwork.Traditionallinkpredictionmethodsoftenusestaticnetworkrepresentations,whichtreatthenetworkasasnapshotataparticulartime.However,staticrepresentationsfailtocapturetheevolutionandtemporaldynamicsofnetworks,resultinginlimitedaccuracyandpredictivepower.Incontrast,dynamicnetworkrepresentationsincorporatetemporalinformation,enablingmoreaccuratepredictionsoflinkformationordisappearanceovertime. 2.DynamicNetworkRepresentation: Dynamicnetworkrepresentationmethodsaimtoencodethetemporalinformationofnetworksintoalow-dimensionalvectorspace.OneofthemostwidelyusedtechniquesisTemporalGraphConvolutionalNetworks(TGCN),whichextendstraditionalGraphConvolutionalNetworks(GCN)tocapturethedynamicnatureofnetworks.TGCNappliesatemporalattentionmechanismtofocusonrecentinteractionsandutilizepreviousrepresentationstopredictfuturelinks.AnotherapproachisDynamicEmbeddingofSocialNetworks(DyETC),whichcombinesrecurrentneuralnetworkswithgraphembeddingtechniquestomodelthedynamicchangesofnetworkstructuresandnodeattributesoverti