基于mmsb模型的微博网络社区发现的研究

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1、AbstractAbstractCommunitydetectionmainlybasedonnodelinkdensenessincommunitiesandlinksparsenesscharacter!sticsbetweencommunities,andwiththehelpofthenetworkaveragepathlength,clusteringcoefficientanddegreedistributionofdepictingthenetworkstructureofattributest

2、odifferentiatecommunity.Thesemethodscan'tfindoverlappingcommunityanditscomputationalcomplexitywillbecomelargerbecauseoftheincreasingofthenetworknodes.Asaresultofthehighnumberofthenetworknodesandthecomplexrelationshipbetweenthenodes,thetraditionalmethodhasli

3、mitationsforMicro-blognetwork.MMSBmodelisakindbasedBaesaetwrkadallwsdesbeltltpledelsthect.Althoughthemodelcandealwithlarge-scalenetworknodes,andthecomputingcomplexityisrelativelylow,butthemodelissuitableforundirectednetwork.BasedontheMMSBmodelandcombinedthe

4、characteristicsofMicro-blog,byimprovingtheoldmodel,IputforwardanewcommunitydetectionmodelWB-MMSBforMicro-blog.ConsideringthenodesofMicro-blogNetworkhavesingledirectionrelations,WB-MMSBisptrwardrctdetectwhchsesdrectededestebdthedrectrelationsofnodes,fromtwoa

5、spectslink-inandlink-outtoquantifythecommunitymembershipofnodes,usesexponentialfamilydistributionandmean-fieldvariationalinferencemethodtoinferencetherepresentationsofvariablesinthismodel,andusesSVIalgorithmtocomputerelatingparameters.ExperimentsadoptSina-W

6、eibodatasetandNMItotesttheperraceWB-MMSB.TheresltsdcatethatthectdetectabilityofWB-MMSBmodelisbetterthanaMMSBmodel,theconvergencerateofWB-MMSBmodelisfasterthanaMMSBmodel.KeywordsMicro-blogNetworkCommunitydetectionMixedmembershipstochasticblockmodelOverlappin

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