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1、IEEETRANSACTIONSONKNOWLEDGEANDDATAENGINEERING,VOL.19,NO.10,OCTOBER20071333CommunityMiningfromSignedSocialNetworksBoYang,WilliamK.Cheung,andJimingLiuAbstract—Manycomplexsystemsintherealworldcanbemodeledassignedsocialnetworksthatcontainbothpositiveandnegativerelations.Algorithmsforminingsocia
2、lnetworkshavebeendevelopedinthepast;however,mostofthemweredesignedprimarilyfornetworkscontainingonlypositiverelationsand,thus,arenotsuitableforsignednetworks.Inthiswork,weproposeanewalgorithm,calledFEC,tominesignedsocialnetworkswherebothpositivewithin-grouprelationsandnegativebetween-groupr
3、elationsaredense.FECconsidersboththesignandthedensityofrelationsastheclusteringattributes,makingiteffectivefornotonlysignednetworksbutalsoconventionalsocialnetworksincludingonlypositiverelations.Also,FECadoptsanagent-basedheuristicthatmakesthealgorithmefficient(inlineartimewithrespecttothes
4、izeofanetwork)andcapableofgivingnearlyoptimalsolutions.FECdependsononlyoneparameterwhosevaluecaneasilybesetandrequiresnopriorknowledgeonhiddencommunitystructures.TheeffectivenessandefficacyofFEChavebeendemonstratedthroughasetofrigorousexperimentsinvolvingbothbenchmarkandrandomlygeneratedsig
5、nednetworks.IndexTerms—Communitymining,agent-basedapproach,randomwalk,signedsocialnetworks.Ç1INTRODUCTIONANYcomplexsystemsintherealworldcanbemodeledthedensityoflinksbutalsobythesignsoflinks.Massocialnetworks[1],[2].AsocialnetworkcanbeQualitativelyspeaking,theyaredefinedasthegroupsofdefineda
6、sagraphG¼ðV;EÞ,whereV¼fv1;...;vngisthenodeswithinwhichthepositivelinksaredenseandsetofvertices,andEisthesetofedgesconnectingpairsofbetweenwhichnegativelinksarealsodense.Thechallengeofminingsignednetworkcommunitiesliesinthefactthatitvertices.Forexample,inahumansocialnetwork,eachisnaturaltoha
7、vesomenegativelinkswithingroupsand,atvertexdenotesanindividualoranode,andeachedgethesametime,somepositivelinksbetweengroups.Findingdenotesarelationoralinkbetweentwonodes.Inweightedthebestandyetrobustpartitionofthenetworkforsocialnetworks,eachlinkisattach
