social and technological network data analytics lecture 2 ... · social and technological network...
TRANSCRIPT
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SocialandTechnologicalNetworkDataAnalytics
Lecture2:SmallWorldandWeakTies
Prof.CeciliaMascolo
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InThisLecture
• Wewillcomparerandomnetworkswithrealnetworks
• Wewillintroducetheconceptofsmallworldnetworks
• Wewillintroducetheconceptofweaktiesandillustratetheirimportance
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ClusteringCoefficientofRealNetworks
• From[WattsandStrogatz,1998]• Characteristicpathlengthandclusteringcoefficientforsomerealnetworksandforrandomnetworkswithsamenumberofnodesandaveragenumberofedgespernode.
• Aimistocheckifrandomgraphscanmodelrealnetworks.
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RealNetworksvs RandomNetworks
• FilmActors:actorsinmoviestogether• Powergrid:thenetworkoftheelectricitygenerators
• C.elegans:networkofneuronsofaworm• LiscomparablewhileCisverydifferent
RandomNet:samesizeandaveragedegree
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SmallWorldModel
• Watts&Strogatz builtamodelwhichwasabletocapturethesecharacteristics.
• Startwithregularlattice– Increaseaprobabilitypof“rewiring”anodetoanothernode.
–Whenpveryhighthelatticewouldbecomearandomgraph.
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SmallWorldModel(2)
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HowareLandCinthismodel?• ThereisazonewhereCishighandLislow• Thesearesmallworldnetworks
RandomNets
Lattice
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OtherRealNetworksExamples
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AnalysisofMessengerNetwork
• [Leskovec andHorvitz2008]analyzedalargedatasetoftheMicrosoftMessenger.
• CommunicationNetworkcontained180millionusersand1.3billionconversationsin1month.
• BuddyNetworkcontained240millionusers.• 99.9%usersbelongedtoaconnectedcomponent.
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AnalysisofaMessengerNetwork• Averageshortestpathis6.6(confirmingMilgram’s study).
• Althoughsomelongerpathsupto29.• Averageclusteringcoefficientisquitehigh:0.137.
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AgainonClusteringCoefficient• Wehaveintroducedtheclusteringcoefficient.Thisindicates:– ThenumberoftrianglesincludingnodeA.– HowconnectedthefriendsofAare.
• Triadicclosure:ifCandBareconnectedtoAthereisanincreasedlikelihoodthattheywillbeconnectedamongthemselvesinfuture.
A B
C DE F
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[Granovetter’74]
• Granovetter interviewedpeopleabouthowtheydiscoveredtheirjobs– Mostpeopledidsothroughpersonalcontacts– Oftenthepersonalcontactsdescribedasacquaintancesandnotclosefriends
• Basicintuitiononthisis:closefriendsarepartoftriadclosuresandwouldknowwhatyouknowandwouldknowotherswhowouldknowwhatyouknow
• Wewillexplainthismoreformally…
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Bridges
• EdgebetweenAandBisabridge if,whendeleted,itwouldmakeAandBliein2differentcomponents
A
B
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LocalBridges
• Anedgeisalocalbridgeifitsendpointshavenofriendsincommon– Ifdeletingtheedgewouldincreasethedistanceoftheendpointstoavaluemorethan2.
A
B
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StrongTriadicClosureProperty(STPC)
• Linksbetweennodeshavedifferent“value”:strongandweakties– E.g:Friendshipvs acquaintances
• StrongTriadicClosureProperty(Granovetter):IfanodeAhastwostronglinks(toBandC)thenalink(strongorweak)mustexistbetweenBandC.
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LocalBridgesandWeakTies• IfnodeAsatisfiestheSTCPandisinvolvedinatleasttwostrongtiesthenanylocalbridgeitisinvolvedinmustbeaweaktie.(Proofbycontradiction)
(assumingSTCP)Ifthereareenoughstrongtiesinthenetworkthenlocalbridgesmustbeweakties
B
CA
S
S
ForACandABtobeastronglinkSCTPsaysBCmustexistbutlocalbridgedefinitionsaysitmustnot
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RealDataValidation
• Granovetter’s theoryabouttheimportanceofweaktiesremainednotvalidatedforyearsforlargesocialnetworksduetothelackofdata.
• [Onnela etal’07]testeditoveralargecell-phonenetwork(4millionsusers):– Edgebetweentwousersiftheycalledeachotherwithinthe18monthsperiod.
– Dataexhibitsagiantcomponent(84%).– Edgeweight:timespentinconversation.
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Onnela etal.2007
• Extendingthedefinitionoflocalbridge• Given:• Neighbourhood overlap:
Numberofnodeswhoareneighbours ofbothA&BNumberofnodeswhoareneighbours ofatleastAorB
• Whenthenumeratoris0thequantityis0.– Numeratoris0whenABisalocalbridge
• Thedefinitionfinds“almostlocalbridges”(~0)
A B
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Neighbourhoodoverlap
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RelationshipofOverlapwithTieStrength
• Red:randomshuffledweightsoverlinks.
• Blue:realones.Correlationwithtiestrength.
Anomaly
Tiestrength:cumulativetiestrengthsmallerthanw
Overlap
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Realtieweightsinaportionofthegraph(aroundarandomnode)
A=RealB=Randomlyshuffled
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Effectofedgeremoval
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Overlapbasedlinkremoval
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Weaktiesmatter!
• Wehavejustseenthatweaktiesmatterandiftheyareremoved,theyleadtoabreakdowninthenetwork.
• Ifstrongtiesareremovedtheyleadtoasmoothdegradingofthenetwork
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Differenceofimportanceofweaktiesinsocialandothernetworks
• Theimportanceofweaktiesisspecifictosocialnetworks
• Inbiologicalandspatialnetworks:– Deletinganimportantroad[strongtie]damagesthenetworkmore
– Acentralveininaleafismoreimportantthansmallerveins
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Tiestrengthmatters:FacebookExample
• Facebook dataanalysisofonemonthofdata• Fournetworks:– Declaredfriendship– Reciprocalcommunication(messages)– Onewaycommunication–Maintainedrelationship:clickingoncontentonnewsfeedfromotherfriendorvisitingprofilemorethanonce.
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Whatdoesitlooklike?(onerandomuser)
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ActiveNetworkSize:numberoflinks
Declaredfriends
Newsfeedeffect
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AnotherstudyonFBshowstheimpactoftiesoverinformationdissemination• 3monthsofFBdata• 253millionusers(profileandlocation)
• Measuringeffectoftiestrengthonsharing
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Howdidtheymeasuretiestrength?
• Privateinteractions• Publicinteractions(comments)
• Coappearance inpictures
• Involvementinthesamepostwithcomments
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Strongtiesaremoreinfluential
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However…
Strongtiesaremoreinfluential buttheireffectisnotlargeenoughtocompensatetheabundanceofweakties…
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TwitterAnalysis
• Huberman atal.haveanalyzedstrongandweaktiesinTwitter.
• The“followers”graphinTwitterisdirected– Someonecanfollowsomeoneelsewhodoesnotfollowhim
• Messagesof140charscanbeposted• Messagescanbeaddressedtospecificusers(althoughtheystayreadabletoall)
• Weakties:usersfollowed• Strongties:userstowhomtheusersentatleast2messagesintheobservationperiod
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Followees
Numbero
fuser’sstrongties
Numberofstrongtiesstaysbelow~50
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Summary
• Smallworldnetworkmodelsareabletocaptureagoodquantityofrealnetworks– Theyhavecharacteristicpathlengthcomparabletorandomnetworks.
– Butmuchhigherclusteringcoefficient.• Wehaveintroducedweakandstrongtiesandshownexampleofapplicationonrealnetworks
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References• Kleinberg’sbook:Chapter3and20.
• Collectivedynamicsof'small-world' networks. Watts,D.J.;Strogatz,S.H.(1998).Nature 393(6684):409–10.
• Structureandtiestrengthsinmobilecommunicationnetworks.J.P.Onnela,J.Saramaki,J.Hyvonen,G.Szabo,D.Lazer,K.Kaski,J.Kertesz,A.L.Barabasi.ProceedingsoftheNationalAcademyofSciences,Vol.104,No.18.(13Oct2006),pp.7332-7336.
• Maintainedrelationships onfacebook.CameronMarlow,LeeByron,TomLento,andItamar Rosenn.2009.On-lineathttp://overstated.net/2009/03/09/maintained- relationships-on-facebook.
• Theroleofsocialnetworksininformationdiffusion.Eytan Bakshy,ItamarRosenn,CameronMarlow,andLada Adamic.2012.InProceedingsofthe21stinternationalconferenceonWorldWideWeb (WWW'12).ACM,NewYork,NY,USA,519-528.
• Socialnetworksthatmatter:Twitterunderthemicroscope. BernardoA.Huberman,DanielM.Romero,andFangWu.FirstMonday,14(1),January2009.