/
DynamicsofTrustReciprocationinMulti-RelationalNetworks*AyushSinghal,Ka DynamicsofTrustReciprocationinMulti-RelationalNetworks*AyushSinghal,Ka

DynamicsofTrustReciprocationinMulti-RelationalNetworks*AyushSinghal,Ka - PDF document

danika-pritchard
danika-pritchard . @danika-pritchard
Follow
367 views
Uploaded On 2016-07-19

DynamicsofTrustReciprocationinMulti-RelationalNetworks*AyushSinghal,Ka - PPT Presentation

fullversionhttparxivorgabs13036385httpswwweverquest2comhttpusbattlenetwowenhttpwwwnickyeecomdaedalusgateway demographicshtml 0 5 10 15 20 25 055 06 065 07 075 08 A ID: 411253

*fullversion:http://arxiv.org/abs/1303.6385https://www.everquest2.com/http://us.battle.net/wow/en/http://www.nickyee.com/daedalus/gateway demographics.html 0 5 10 15 20 25 0.55 0.6 0.65 0.7 0.75 0.8 A

Share:

Link:

Embed:

Download Presentation from below link

Download Pdf The PPT/PDF document "DynamicsofTrustReciprocationinMulti-Rela..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

DynamicsofTrustReciprocationinMulti-RelationalNetworks*AyushSinghal,KarthikSubbian,JaideepSrivastavaUniversityofMinnesota,Minneapolis,MN.ayush,karthik,srivastaTamaraG.Kolda,AliPinarSandiaNationalLaboratories,Livermore,CA.tgkolda,apinar@sandia.gov„Understandingthedynamicsofreciprocationisofgreatinterestinsociologyandcomputationalsocialscience.TherecentgrowthofMassivelyMulti-playerOnlineGames(MMOGs)hasprovidedunprecedentedaccesstolarge-scaledatawhichenablesustostudysuchcomplexhumanbehaviorinamoresystematicmanner.Inthispaper,weconsiderthreedifferentnetworksintheEverQuest2game:chat,trade,andtrust.Thechatnetworkhasthehighestlevelofreciprocation(33%)becausethereareessentiallynobarrierstoit.Thetradenetworkhasalowerrateofreciprocation(27%)becauseithas *fullversion:http://arxiv.org/abs/1303.6385https://www.everquest2.com/http://us.battle.net/wow/en/http://www.nickyee.com/daedalus/gateway demographics.html 0 5 10 15 20 25 0.55 0.6 0.65 0.7 0.75 0.8 ADDITIONAL DAYS OF TRADE INTERACTIONSAUC TRUST TRUST+TRADE TRUST+TRADE+HMPLY TRUST+HMPLY Fig.1:FigureshowsimprovementinAUCbyaddingtradeinteractionforpredictingtrustreciprocation.Dynamicsofthereciprocationvariesfromnetworktonetworkdependingonthelevelofbarrierforreciprocation.Thebarrierforreciprocatingatrustrelationshipcouldbelackofresourcesorhighriskinvolved.Needlesstosay,thesebarriersaffectthelevelsofreciprocationsigni“cantlyindifferentnetworks.Forinstance,inachatnetworkusershaveaverylowbarrierlevelfortrustingeachotherasthereisnocommitmentfromeithersidetoparticipateinanyinvolvedrelationshipor y sis and Minin g 661 A SONAM'13, August 25-29, 2013, Niagara, Ontario, CAN Copyright 2013 ACM 978-1-4503-2240-9 /13/08 ...$15.00 reciprocaledges,degree,andsizeofmodelednetwork.Duraketal.[]proposeanullmodelfordirectedgraphsthatcombinesthereciprocalandtheone-wayedgestogeneratedirectedgraphsthatmatchthein-,out-,andreciprocal-degreedistributions.Ahmadetal.[]usetheoriesofsocialexchangeasabasisforbuildingagenerativemodelGTPAformodelingtemporallyevolvingdirectednetworks.Thereareotherrecentworks,suchasGralaschelliandLoffredo[]thatusestatisticalmeasurestoconcludethatthereciprocationofanetworkisneveratrandomanditiseitherre-ciprocaloranti-reciprocal.Similarly,Zamora-Lopezetal.[proposeamethodtocomputetheexpectedreciprocationofthenetworkasafunctionofin-andout-degreedistributions.Reciprocationinweighteddirectednetworks,especiallyinlargescalemobilecommunicationnetworks,isdiscussedinin14],[].Szelletal.[]showthatnegativeinteractionshavealowerreciprocationcomparedtothepositiveinteractions.Reciprocationhasalsobeenusedforlinkprediction[],[[19].II.MMOGDATAInthissection,wedescribethenetworksusedinourexperiments.A.TrustNetworkInEQII,playersformteamsinordertocompletethegametasks.Astheplayersarelimitedbythenumberofitemstheycancarryatatime,playersbuyhousesasatemporarystoragetoretaintheirarmoryandotheraccessories.Throughthetrustnetwork,playerssharetheirhouseaccesswithotherplayers.Forthisreason,wealsorefertothisnetworkasthetrustnetwork.Wehave9monthsofdatafromJan-01-2006toSep-11-2006with63684nodesand140514edges.Eachnodeinthenetworkisaplayercharacterinthegame,andeachedgeisapermissionlevelgrantedbythecharactertoanothercharacter.Eachedgehasatimestampwhentheaccesswasgranted.Thetrustlevelsaredescribedasfollows.Trustee:Playercanstore,touch,move,add,andre-movethings,andhasalmostsameaccessastheowner.Friend:Playercanstore,touch,andmovethings.Visitor:Playercanenterthehouseandviewthings.:Playercanseethehouseexternallybutcannotenterit.B.TradeNetworkIntheEQIItradenetwork,playersexchangegoodsforcoinsorgoods.Inthisactivity,atradelinkisestablishedbetweentheseller(initiator)andthebuyer(acceptor)inthetradenetwork.Weanalyzedsuchatradenetworkcontaining295,055nodesand11,913,994edgesoveraperiodof9monthsfromJan-01-2006andSep-11-2006.C.ChatNetworkThechatnetworkisacommunicationmediumwhereplay-ersexchangeinstantmessages.Thenumberofnodesinthisnetworkis349,654,andthenumberofedgesis86,948,748,spanningoveraperiodofonemonthfromJul-29-2006toSep-D.NetworkPro“lesWepresentthedegreedistributionofthesenetworksin.Thedistributionswereconstructedusingsnapshotsofdifferentnetworksovertheentireobservationperiod.ThedistributionsseemtofollowthepowerlawwithexponentTABLEI:Statisticsofreciprocationintrade,chatandtrustnetworks. Network AllForward First Total Type(period) Edges Reciprocation Reciprocation Chat(1month) 1840492 441039(23.9%) 599548(32.6%) Trade(9months) 520861 74137(14.23%) 136809(26.3%) Trust(9months) 62674 8452(13.5%) 8083(14.0%) ofthepowerlawrangingfrom1to3.Theexponentwascalculatedusingtheslopeofaleastsquares“tinthelog-logIII.RECIPROCATIONINIFFERENTETWORKSThereciprocationofanetworkistheratioofforwardedges(sayfromplayer)thathaveacorrespondingbackwardedge(fromplayer),i.e.,theratioofmutualinteractions.A.BarriersofReciprocationBarriersofreciprocationcanbebroadlygroupedintoriskandutility.Theriskfactorsincludeloosinganasset,in-gamepoints,orin-gametime;andtheutilityinclude,immediategainsintermsofpointsandassetsandlongtermfutureprospects.Eachnetworkhasspeci“ccharacteristicsthatleadtolow,mediumorhighbarriersforreciprocation.Accordingly,trustnetworkhasthelevelofbarrier.Inthecaseoftradenetwork,thebarrierofreciprocationfallintobarriercategory.ThechatnetworkfallsintothelowbarrierB.MultiplereciprocationsTherecanbeseveraloverlappingforwardandbackwardarcsbetweeneachpairofplayers.Forthepurposeofmeasur-ingthereciprocationandresponsetime,we“rstpartitionthetimelineintoseveralpartitions.Weconsiderthestarttimeof“rstforwardedgeandthecorrespondingendtimeofthe“rstresponseasthe“rstpartition.Similarly,thesecondforwardarcanditsresponsemarkstheendofthesecondpartitionandsoon.Fortheremainderofthispaper,unlessspeci“ed,wealwaysrefertotheoverallreciprocationrate.InTableshowmultiplereciprocationrateforthreedifferentnetworks.C.ReciprocationinTrust,ChatandTradeNetworksInthissection,westudythereciprocationbehaviorintrust,chatandtradeasindependenthomogenousnetworks.Then,inthenextsection,weanalyzetheinteractionsofchatandtraderelationshipsinaffectingthereciprocationofthetrustnetwork.Thechatandtradenetworkshavesimpleedgetypeswithnoattributesexcepttimestamps.Forthetrustnetwork,theTrustlevelcorrespondstobeingaTrustee.Wecollapsethelowertrustlevels,(Friend,Visitor,andNone),toasingleNot-Trustlevel.WesummarizethetotalreciprocationforeachnetworktypeinTable.Inthetrustnetwork,beingahighbarrierrelationship,only14.0%oftheforwardtrust(8803one-way)linksreceiveatrustresponse(reciprocation)backandtheirresponsetimedistributionisshowninFigure.Asthechatnetworkisalowbarrieractivity,ithasthehighestamountofreciprocationwith32.6%offorwardedgesreciprocated.Thelowbarrierinthisnetworkisduetotheinstantnatureofthecommunicationandminimalriskinvolvedinmakingachatreciprocation.Oncontrary,inthemediumbarriertradeactivitiesthereciprocationrateis26.3%,lowerthanchatbutmorethanthetrustnetwork.Thereasonswhyaplayermaynot y sis and Minin g 662 100 101 102 100 101 102 103 104 105 DEGREEFREQUENCY INŠDEGREE OUTŠDEGREE 100 101 102 103 104 100 101 102 103 104 105 DEGREEFREQUENCY INŠDEGREE OUTŠDEGREE 100 101 102 103 100 101 102 103 104 105 DEGREEFREQUENCY INŠDEGREE OUTŠDEGREE Fig.2:Degreedistributionsoftrust(left),trade(right-top)andchatnetworks(right-bottom). 100 101 102 100 101 102 103 104 RESPONSE TIME IN DAYSNUMBER OF RESPONSES RESPONSE TIME m=27.304 s=42.812 #examples= 8803 (a)Trust 100 101 100 101 102 103 104 105 106 RESPONSE TIME IN DAYSNUMBER OF RESPONSES RESPONSE TIME m=0.317 s=1.397 #examples=599548 (b)Chat 100 101 102 100 101 102 103 104 105 NUMBER OF RESPONSESRESPONSE TIME IN DAYS RESPONSE TIME m=12.587 s=25.954 #examples=136809 (c)TradeFig.3:Theresponsetimedistributionforthethreenetworks.reciprocateinthisnetwork,couldbelackofeitherresourcesoraneedfordoingso.Further,wewishtoanalyzetheresponsetimedistributionsofthesenetworkstounderstandsomekeyquestions,suchas,doesallreciprocationsoccurwithinacertainnumberofdaysoraretheyspreaduniformlyoveralongerperiodoftime?showstheresponsetimedistributionforthetrustrelationship.We“ndthattheresponsetimedistributionfollowsapowerlaw,withameanresponsetimeof27days.FortheChatactivity,theresponsetimedistributionisshownin“gure.Thedistributionroughlyfollowsapowerlaw.Thereisanoutlierregionaround45days.Weinvestigatedthisregionandfoundthattheseare“rsttimeuserswhoarenotfamiliarwiththesystem.Wenotethatsuchusersareextremelyrareinthedataset(lessthan0.01%).The“gurealsoshowsthatmostoftheusersinthelowbarrier,chatnetworkreciprocatewithinthesamedayoratmostthenextday.Thisisevidentsincethemean“rstresponsetimeinthechatnetworkislessthanoneday(0.317).Inthechatnetworkthereisasharptruncation[after7days,asthesigni“canceofamessagebeyondaweekbecomescompletelyirrelevanttothecontextofthegame.Forthetradenetwork,weshowtheresponsetimedistribu-tioninFigure;thedistributionhasaheavytailandseemstofollowapowerlaw.Theslopeofthisdistributionisnotassteepasthatofthechatnetwork,implyingthetradereciprocationisnotasquickasinchat.Asthebarrierforreciprocationinthetradenetworkismorethanchat,theaverageresponsetimeintradeis43Xslowerataround13days.IV.RECIPROCATIONINETEROGENEOUSNETWORKSWewillnowoverlaythetrust,chat,andtradenetworkstoanalyzetheinteractionsamongheterogeneousedges.AsTABLEII:Thereciprocationcountsfor“rstinteraction(“rstforwardrequestand“rstreply)inaheterogeneousMMOGnetworkforaperiodofonemonth. Forward FirstForward Chat Trade Trust Type Edges Reciprocation Reciprocation Reciprocation Chat 1645623 435758 1187 105 Trade 74428 7953 11402 335 Trust 10502 907 1016 722 thechatdataisavailableonlyforamonth,werestricttheotherdatasetsalsotothisonemonthperiod.Thefocusofthissectionistoanswerquestions,suchas:Howmanytimesdoesatrustgrantingfromplayerresultinatrustreciprocation?Doesaplayerprefertoreciprocatewithtradeorsomeotherlowbarrierinteractionsuchaschatbeforegrantingahighbarrierrelationshipsuchastrusttoplayer.The“rstinteraction,asnotedearlierinFigure,capturesallthecharacteristicsoftheadditionalinteractions,henceweconsideronlythe“rstinteractionforthisexperiment.Foreachforwardedgetype,wecountthenumberof“rstreciprocationbetweenpairsofplayersintheconsolidatednetwork.Inthecaseoftiereciprocationsacrossmultipleedgetypes,weincludeallthetiededgeswhilecounting.WehavesummarizedthereciprocationsforeachforwardinteractiontypeinTableAsweseefromTable,theplayerswhoperformchatpre-dominantlymakeareciprocationusingthechatlink(26.48%).Thesameobservationismadeforthetradeactivity,whichisalsoalowbarrieractivity(buthigherthanchat).Herealsowe“ndthattraderesponsesarethepredominanttypeofresponsesforatraderelationship.However,thisisnotthesameinthe y sis and Minin g 663 caseofatrustforwardedgetypesincetrustisahighbarrierrelationshipandamoretimeconsumingactivity.Sopeopleareverycarefulbeforereciprocatingforsuchrelationshipsandreciprocationsare“rstinitializedthroughlowbarrieractivitiesbeforereciprocatingwithahighbarrierrelationship,suchastrust.Thereciprocationsfortrustforwardedgeispredominantlythroughchat(8.63%)ortrade(9.67%).Wenowanalyzethedependencybetweentrustrelationshipagainsttradeandchatinteractions.Theaimofthisexperimentistoquantifyhowchatandtrade,lowbarrierrelationships,in”uencereciprocationinahighbarrierrelationship.Foranytwonodesinthetrustnetwork,westartouranalysisfromthetimewhenaforwardTRUSTedgefromisestablished.ForsuchaforwardedgetherecaneitherbeaTRUSTreplytocompletetheTRUSTrelationshipornoTRUSTreply(incompleteTRUSTrelationship).TheTRUSTrelationshipisdeterminedasincompleteifthereisnotrustresponsefromwithintheaveragetrustresponsetimewhichis4.6daysinthiscase.Inotherwords,wetruncatetheresponsetimefortheincompletereciprocationsbythemeanresponsetime(4.6days)anduseonlytheperiodbeforethismeanresponsetimeforfurtheranalysis.Therecanalsobeseveralotherresponses(lowbarrierinteractions)fromreplieswithaTRUSTlink.Understandingtheseotherrelationships,suchaschatandtrade,beforeaTRUSTreplyisformedfromiscrucialtodecipherthenatureofsocializationrequiredforahealthymutualtrustrelationship.Asaresultofthisexperiment,we“ndthatcompleteandincompleteTRUSTdifferfromeachotherintermsofchatandtraderesponses.Weobservethattheresponsesareexactlyinanoppositeorderinthetworows.ForthecompleteTRUSTweobservethattherewere743forwardsedgesthatwererespondedbackwithTRUST.However,beforetheTRUSTiscompletedbetween,weseethatthereare408traderesponsesfrom.Thesetraderesponsesaccountfornearly63%ofthetotalresponses.Wehaveonly243chatresponses,whichiscomparativelysmallerthanthetraderesponses.Surprisingly,theamountoflowbarrierresponsesfortheincompleteTRUSTgetscompletelyreversed.Thereare9145forwardTRUSTedgeswhichdonotgetaTRUSTreplybackandremainonesided.FortheseTRUSTrequests,we“ndthatthechatresponsesfromnow6962(approximately75%ofthetotalresponses)whilethetraderesponsesaresigni“cantlylower(aslowas25%).Thisexperimentcon“rmsthataTRUSTrelationshipbe-ismorelikelytocompleteiftherearemoretraderesponsesthanchatresponsesfrom.ThisinterestingresultcanbeusedtoinferthefutureTRUSTrelationshipsbasedonsomelowbarrierrelationshipssuchaschatandtrade.V.PREDICTINGTRUSTRECIPROCATIONInthissectionweevaluatehowwellcanwepredictahighbarrierrelationship,suchasatrust,usinginformationaboutthemediumbarrieractivitiesbetweenthenodes.Theempiricalanalysisintheprevioussectionshowedthatthesuccess(completion)ofhighbarriertrustrelationshipdependsonsomemediumbarrierrelationshipliketrade.Weusethisasourmotivationtoquantifyhowwellthemediumbarrierrelationshipcanhelptopredicthighbarrierrelationshipcom-pletion.However,weusetheentire9monthsofdatainordertomakeanyconclusionsforthetrustreciprocationprediction.ThechatrelationshiphastobeexcludedfromthisexperimentTABLEIII:Tablecomparingreciprocationpredictionaccuracyusingdifferentfeaturesets. Average Average Classi“er CWA AUC Precision Recall F-measure onlytrust 0.515 0.659 0.800 0.863 0.806 trust+trade(K=0) 0.526 0.637 0.825 0.866 0.816 trust+homophily 0.519 0.604 0.788 0.849 0.808 trust+trade(K=0)+homophily 0.527 0.636 0.826 0.866 0.817 trust+trade(K=20) 0.588 0.714 0.871 0.885 0.851 becauseofitslimitedavailabilityforasinglemonth.ButweaddseveralotherfeaturestomaketheexperimentmoreForthetrustnetworkdatafor9months,thereareatotalof61006trustrequests(forwardsedges).Thetrustlinksthatarereciprocatedis8252whereas52574forwardsedgesremainedincomplete.Fortheforwardrequests,weignoredtherequeststhatstartedinthelast(9th)monthbecauseitishardtodeterminewhethertherequestswerecompleted.Thusthenumberoftrustrequestsareslightlylessthanthosementionedinearliersections.Sothecompletedtrustlink(reciprocated)formsoneclassandtheincompletetrustlinks(unreciprocated)formsthesecondclass.Thefollowingfeatureswillbeusedtobuildthepredictionmodel.Featuresfromhighbarrierrelationship(trust):Thesefeaturescharacterizethepositionofplayers(nodes)inthetrustnetwork.ForthereciprocationlinksA,Bweconsidertwostructuralfeatures.The“rststructuralfeaturedescribestheconnectivityoftoothernodesinatrustnetwork.Thesecondstructuralfeatureistheconnectivityofwithothernodesinthetrustnetwork.Forconvenience,wereferthesefeaturesastrustŽfeatures.Featuresfrommediumbarrierrelationship(trade)Thisfeaturesetconsistsoffeaturesfromthreesub-categoriesnamely,structural,past-behavioralandfuture-behavioral.ForaA,Bthestructuralfeaturecorrespondstothedegreeofanddegreeofinthetradenetwork.Thepast-behavioralfeaturesforalinkA,Bcorrespondtothecountofthetradeinteractionofthetypebeforethetrustrequestfromstarted.Thisfeaturetakesintoaccountthetradebehaviorbetweenbeforeanytrustinteractionstartedbetweenthem.Thefuture-behavioralfeaturestakesintoaccountthebehavioroftradeinteractionbetweenoncethetrustrequestissentfrom.Hereweuseatimewindow(indays)startingfromthetimewhentrustrequestwasinitiatedfrom.WecountthenumberoftradeinteractionsinthistimewindowFeaturesfromplayerdemography(homophily):Inthisfeaturesetwetakeintoaccountthetwotypesofhomophilies.The“rsttypeofhomophilyisgenderhomophily.Thegenderhomophilybetweenis1ifhasthesamegender,and0otherwise.Theseconddemographicfeatureistheexperiencehomophilybetween.ItiscomputedA,BtheexperiencelevelofAsmentionedearlier,theaimofthisexperimentistoquantitativelycomparetheimpactofusingdifferentfeatures(describedabove).Weusedthesefeaturesetstobuildbinaryclassi“er(randomforestdecisiontree)topredictwhetheratrustreciprocationwithhappenornot.Tablecomparesthetrustreciprocationaccuracyforvariousfeaturesets.Theadditionoftradefeaturesbooststheperformanceofthepredictivemodeloverthecasewhenno y sis and Minin g 664 0 5 10 15 20 25 0.5 0.52 0.54 0.56 0.58 0.6 0.62 ADDITIONAL DAYS OF TRADE INTERACTIONCWA TRUST TRUST+TRADE TRUST+TRADE+HMPLY TRUST+HMPLY (a)Classweightedaccuracy 0 5 10 15 20 25 0.8 0.81 0.82 0.83 0.84 0.85 0.86 0.87 ADDITIONAL DAYS OF TRADE INTERACTIONF1 MEASURE TRUST TRUST+TRADE TRUST+TRADE+HMPLY TRUST+HMPLY (b)F1measure 0 5 10 15 20 25 0.75 0.8 0.85 0.9 0.95 ADDITIONAL DAYS OF TRADE INTERACTIONPRECISION TRUST TRUST+TRADE TRUST+TRADE+HMPLY TRUST+HMPLY (c)Precision 0 5 10 15 20 25 0.84 0.85 0.86 0.87 0.88 0.89 0.9 0.91 ADDITIONAL DAYS OF TRADE INTERACTIONRECALL TRUST TRUST+TRADE TRUST+TRADE+HMPLY TRUST+HMPLY (d)RecallFig.4:Comparingclassweightedaccuracy,precision,andrecallfortrustreciprocationprediction.tradefeaturesareused.Weextendthisexperimenttoincludethevariationoffuturetimewindowsize(K)forallfeaturesandmonitoritsimpactintermofaccuracyofthepredictionmodel.Figureshowstheresultsofthisexperiment.Asmentionedearlier,westudytheimpactofvaryingthetimewindowssize(from0to25days)forallthefeatures.AsshowninFigures,usingtradeasanadditionalfeatureinthepredictionmodeloutperformsthemodelwhichusesonlytrustortrustandhomophilyonly.Wealso“ndthatadditionofhomophilyfeaturesdonothaveasigni“cantimpactinpredictingreciprocationintrustnetwork.Thisisaninteresting“ndingfortrustreciprocationpredictionbecauseitisageneralnotionthathomophilyissigni“cantinpredictingtrustlinks[].Apossibleexplanationofthisob-servationisthatthereciprocationphenomenonissigni“cantlydifferentfromanormaltrustformationphenomenon.Asweknow,inreciprocationthereisalreadyaonesidedrelationshipestablishedandareciprocationmightdependonentirelyotherdynamicssuchastradeinteractions.VI.CWehavestudiedvarioussocialfactorsaffectingrecip-rocationinthreedifferentinteractionnetworksfromSonyEverQuestIIMMOG.We,usingresponsetimeanalysis,showthatpeopleareslowinbuildingmutualhightrustrelationshipscomparedtolowbarrierones.Weextendouranalysisfromsingle-typenetworkstoheterogeneousnetworks,wherewecon“rmthathighdegreeofmediumbarrieractivitiesiscrucialforreciprocationinhighbarrierrelationships.CKNOWLEDGMENTSThisworkwasfundedbytheGRAPHSprogramatDARPA.SandiaNationalLaboratoriesisamulti-programlaboratorymanagedandoperatedbySandiaCorporation,awhollyownedsubsidiaryofLockheedMartinCorporation,fortheU.S.DepartmentofEnergysNationalNuclearSecurityAdministrationundercontractDE-AC04-94AL85000.[1]D.Lazer,A.Pentland,L.Adamic,S.Aral,A.-L.Barabsi,D.Brewer,N.Christakis,N.Contractor,J.Fowler,M.Gutmann,T.Jebara,G.King,M.Macy,D.Roy,andM.V.Alstyne,Computationalsocialscience,Žvol.323,no.5915,pp.721…723,2009.[2]N.Yee,Motivationsforplayinonlinegames,ŽCyberpsychologyand,vol.9,no.6,pp.772…775,2006.[3]E.Castronova,SyntheticWorlds:TheBusinessandCultureofOnline.UniversityOfChicagoPress,2005.[4]M.SzellandS.Thurner,Measuringsocialdynamicsinamassivemultiplayeronlinegame,ŽSocialNetworks,vol.32,no.4,pp.313…329,2010.[5]A.W.Gouldner,Thenormsofreciprocity:Apreliminarystatement,ŽAmericanSociologicalReview,vol.25,no.2,pp.161…178,1960.[6]S.Leider,M.M.Mbius,T.Rosenblat,andQ.-A.Do,Directedaltruismandenforcedreciprocityinsocialnetworks,ŽTheQuarterlyJournalof,vol.124,no.4,pp.1815…1851,2009.[7]K.V.Hansen,Theaskingrulesofreciprocityinnetworksofcareforchildren,ŽQualitativeSociology,vol.27,no.4,pp.421…437,2004.[8]C.A.Bliss,I.M.Kloumann,K.D.Harris,C.M.Danforth,andP.S.Dodds,Twitterreciprocalreplynetworksexhibitassortativitywithrespecttohappiness,ŽJournalofComputationalScience,vol.3,no.5,pp.388…397,2012.[9]V.ZlaticandH.Stefanc,Modelofwikipediagrowthbasedoninformationexchangeviareciprocalarcs,ŽEPL(EurophysicsLetters)vol.93,no.5,p.58005,2011.[10]N.Durak,T.G.Kolda,A.Pinar,andC.Seshadhri,Ascalablenullmodelfordirectedgraphsmatchingalldegreedistributions:In,out,andreciprocal,ŽinProc.IEEE2ndWorkshoponNetworkScience,2013.[11]M.A.Ahmad,D.A.Huffaker,J.Wang,J.W.Treem,M.S.Poole,andJ.Srivastava,GTPA:Agenerativemodelforonlinementor-apprenticenetworks,Žin,2010.[12]D.GarlaschelliandM.I.Loffredo,PatternsofLinkReciprocityinDirectedNetworks,ŽPhys.Rev.Lett.,vol.93,p.268701,2004.[13]G.Zamora-Lopez,V.Zlatic,C.Zhou,H.StefanCic,andJ.Kurths,Reciprocityofnetworkswithdegreecorrelationsandarbitrarydegreesequences,ŽPhys.Rev.E,vol.77,p.016106,2008.[14]L.Akoglu,P.V.deMelo,andC.Faloutsos,Quantifyingreciprocityinlargeweightedcommunicationnetworks,ŽinPaci“c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD),2012,pp.85…96.[15]C.Wang,A.Strathman,O.Lizardo,D.Hachen,Z.Toroczkai,andN.V.Chawla,Weightedreciprocityinhumancommunicationnetworks,ŽarXivpreprintarXiv:1108.2822,2011.[16]M.Szell,R.Lambiotte,andS.Thurner,Multirelationalorganizationoflarge-scalesocialnetworksinanonlineworld,ŽProc.NationalAcademyofSciences,vol.107,no.31,pp.13636…13641,2010.[17]J.Cheng,D.M.Romero,B.Meeder,andJ.M.Kleinberg,Predictingreciprocityinsocialnetworks,ŽinIEEESocialComm/PASSAT,2011,pp.49…56.[18]V.ZlaticandH.Stefancic,In”uenceofreciprocaledgesondegreedistributionanddegreecorrelations,ŽPhysicalReviewE,vol.80,no.1,p.016117,2009.[19]M.A.Ahmad,Z.Borbora,J.Srivastava,andN.S.Contractor,Linkpredictionacrossmultiplesocialnetworks,ŽinICDMWorkshops,2010,pp.911…918.[20]L.A.N.Amaral,A.Scala,M.Barthemy,andH.E.Stanley,Classesofsmall-worldnetworks,ŽProc.NationalAcademyofSciences,vol.97,pp.11149…11152,2000.[21]M.Ahmad,I.Ahmed,J.Srivastava,andM.Poole,Trustme,imanexpert:Trust,homophilyandexpertiseinmmos,ŽinIEEESocial-Comm/PASSAT,2011,pp.882…887. y sis and Minin g 665

Related Contents


Next Show more