cascade prediction
TRANSCRIPT
SocialMedia
Social media are computer-mediated tools that allow people,companies and other organizations to create, share, orexchange information, career interests, ideas, andpictures/videos in virtual communities and networks.
Wikipedia
TypicalExamples:
Twitter,Facebook,Weibo,WeChat,Science,…
WhatIwilltalkabout
• CascadePrediction– Shen etal.,AAAI2014;Bao etal,2015;Gao etal.,WWW2016
• Influencemaximization– Chengetal.,CIKM2013;Chengetal.,SIGIR2014
• Collectivebehavior– Shen etal.,PNAS,2014;Scientometrics,2016
PartI
CascadePrediction
Cascades
• Threekindsofcascades– 𝑡", 𝑡$,⋯ , 𝑡&,⋯ 𝑡'
• PageView,queryusage,……
– 𝑢", 𝑡" , 𝑢$, 𝑡$ ,⋯ , 𝑢), 𝑡) ,⋯ , 𝑢', 𝑡'• WeChat,epidemics,……
– 𝑢", 𝑣", 𝑡" , 𝑢$, 𝑣$, 𝑡$ ,⋯ , 𝑢), 𝑣), 𝑡) ,⋯ , 𝑢', 𝑣', 𝑡'• Weibo,citation,……
Arethepopularitydynamicspredictable?(Power-lawdistribution,Null-model,……)
Yearly citation c(t) for 200 randomly selected papers published between 1960 and 1970 in the PR corpus. The color code corresponds to each papers’ publication year.
CascadepredictionHeterogeneousPopularitydynamics
Shenetal.,AAAI2014
Cascadeprediction
C.S.Peirce.TheNumericalMeasureoftheSuccessofPredictions.Science,4(93):453-454,1884.
Burst:temporalscale-free
Shenetal.,AAAI2014
Cascadeprediction• Feature-basedmethods
– Extractfeatures,e.g.,contentfeature,userfeature,structuralfeature,andthenpredictpopularityusingstandardclassificationorregressionmodels[Lerman etal.,WWW2010;Bao etal.,2013]
• TemporalAnalysis– Treatpopularitydynamicsastime
series,makingpredictionsbyeitherexploitingtemporalcorrelations[SzaboandHuberman,Comm.ACM2010;YangandLeskovec,ICDM2010;Gomez-Rodriguezetal,ICML2013]
Thesemethodsignorethatpopularitydynamicsisaprocessreflectingthearrivalofcollectiveattention.
Popularityinearlystage
Structuraldiversity
Cascadeprediction
Keyfactorstopopularitydynamics
IntrinsicAttractiveness…………….
Richgetsricher.……………………
Agingeffect……………………………….
⌘i
cti
Pi(t) =1p
2⇡�itexp
✓� (ln t� µi)
2
2�2i
◆
Arrivalrateofattentiontoanitemisdeterminedby
Visibility?
Power-law,log-normal,exponential,Rayleighdistribution
Shenetal.,AAAI2014
CascadepredictionGenerativemodelofcitationsdynamics:
ReinforcedPoissonProcess(RPP):𝜃, = {𝜇,, 𝜎,}
MLEforparameterestimation: Prediction:
Attractiveness
Agingeffect
Richgetsricher
Shenetal.,AAAI2014
Quantifyingscientificimpact
t̃ ⌘ (ln t� µi)/�i
c̃ ⌘ ln(1 + cti/m)/�i
c̃ = ��t̃�
Bonner&Fisher,Linearmagneticchainswithanisotropiccoupling,PhysicalReview (1964)Hohenberg &Kohn, Inhomogeneous electron gas,PhysicalReview (1964)Bardakci etal.IntrinsicallyBrokenU(6)⊗ U(6)SymmetryforStrongInteractions,PhysicalReviewLetters(1964)Berglund&W.E.Spicer,Photoemissionstudiesofcopperandsilver:Theory,PhysicalReview(1964)
1.1 4.8 1.13.0 8.8 1.21.9 7.5 0.96.7 9.2 1.0
Examples:
Quantifyingscientificimpact
Wang,Song,&Barabási,Science,2013.
Universalcitationdynamicsfordifferentjournals:
CascadepredictionCitation dynamics
ü RPP(ReinforcedPoissonProcess)consistentlyoutperformscompetingmethods.ü RPPwithoutpriorperformsalmostidenticallytoRPPwithprior(highaccuracy),but
performsremarkablybadonahandfulofcases,causedbyoverfitting (highMAPE)ü ThesuperiorityoftheRPPwithprior,increaseswiththelengthoftrainingperiods.
CascadepredictionExtensionsoftheRPPmodel
Ø Replacetherelaxationfunctionwithotherformoffucntions[Gao etal.,WSDM2015]
Ø Replacingthe“rich-gets-richer”mechanismwithobservedvisibility[Zhaoetal.,KDD2015]
Ø MixtureofRPPtomodelmultiplediffusion[Gao etal.,WWW2016]
e.g.,Numberofretweetersà Followercountofeachretweeter
e.g.,lognormalà exponentialorpowerlawfunction
Shenetal.,AAAI2014
CascadepredictionMixtureofRPPmodel
Diffusionprocesswithmultiplestages
EachcomponentisaRPPmodel
CascadepredictionHawkesProcess
Ratefunction:
Attractivenessorinfectiousnessofmessages
Zhaoetal.,KDD2015;Bao etal.,WWW2015
CascadepredictionRecurrentMarkedPointProcess
Idea:Learningtheratefunctionfromdata,insteadofhuman-definedratefunction
Duetal.,KDD2016
ü Embeddingeventhistorytovector
ü Learnratefunctionwithintheframeworkofmarkedpointprocess
CascadepredictionSummary
• Popularitydynamicsisanarrivalprocess,capturinghowamessageaccruesattention
• Feature-basedmethodwillbeincorporatedwithfeature-learningmethod
Popularitypredictionisstillanopenproblem!!
PartII
InfluenceMaximization
InfluencemaximizationProblemdefinition
– Input: l A social network G=(V,E), with V being the
node set and E being the edge setl Diffusion model:independent cascade
model and linear threshold modell k:number of seed nodes
– Output: a set of seed nodes S, | S | ≤ k– Objective: maximize the spread of
influence 𝜎 𝑆
0.1 0.2
0.3 0.1
0.1
0.5
0.4
0.1
0.4 0.4
0.2
0.2
0.10.5
0.3
Spread of influence
Given a social network, influence maximization aims to find a size-fixed set of seed nodes, maximizing the spread of influence.
Chengetal.,CIKM2013
InfluencemaximizationExistingmethods:
• Greedyalgorithm– Select,onebyone,thenodewithmaximummarginalinfluencetoaddintothesetofseednodes[Kempe etal,KDD2013;Leskovec etal.,KDD2007]
– Withguaranteedaccuracy1-1/e- ϵ,butnotscalable
• Heuristicalgorithm– Selectseednodeseitherbydelegatemetrics,e.g.,degree,PageRank,orestimateinfluencespreadwithapproximatemethods[Chenetal.,KDD2009;Jungetal.,ICDM2011]
– ScalablebutwithoutguaranteedaccuracyWelackanaccurateandscalablealgorithmtosolvetheproblemof
influencemaximization.
InfluencemaximizationPropertiesofIM
• Propertiesof𝜎 𝑆– Non-negative
– Monotone
– Submodular
argmax9𝜎 𝑆
st. 𝑆 ≤ 𝑘
𝜎 𝑆 ≥ 0, foranyS
𝜎 𝑆 ≤ 𝜎 𝑇 , ifS ⊆ T
𝜎 𝑆 ∪ {𝑣} − 𝜎 𝑆 ≥ 𝜎 𝑇 ∪ 𝑣 − 𝜎 𝑇 , ifS ⊆ T
Greedyalgorithmachievestheaccuracy1-1/e- ϵ ifthevlueof𝜎 𝑆couldbeexactlycomputed.
Influencemaximization(IM)
InfluencemaximizationScalability-accuracydilemma
• Valueof𝜎(𝑆)cannotbeexactlycomputed– MonteCarlosimulationisusedtoapproximatelyestimate𝜎(𝑆)
• Scalability-accuracydilemma– IncreasethenumberofMonteCarlosimulation
• Estimationof𝜎(𝑆)becomesaccurate,ϵ decreases• Lowscalability
– DecreasethenumberofMonteCarlosimulation• Highscalability• Estimationof𝜎(𝑆)becomesinaccurate,ϵ increases
Chengetal.,CIKM2013
InfluencemaximizationOursolution:StaticGreedy
• Idea: reusethesamesetofMonteCarosimulation
IndependentMonteCarlosimulations(𝑁 → ∞)
ReusethesamesetofMonteCarlosimulation(smallR)
…
𝜎(𝑆) 𝜎(T)
…
vs.
…
𝜎(𝑆) 𝜎(T)
vs.
…
1…N 1…N 1…R 1…R
Monotonicityandsubmodularity arestrictlysatisfied.Scalability-accuracydilemmaissolved.
InfluencemaximizationStaticGreedy:Results
𝑑 Q,R
log 𝑅
*,
, *
( ) ( )( )
k R kR k
k
I S I Sd
I S-
=
Run
ning
tim
e
Datasets
Our method
Our method
• NumberofMone Carlosimulationsdecreasesby2-3ordersofmagnitudes
• Runningtimedecreasesby3-4ordersofmagnitudes
Chengetal.,CIKM2013
InfluencemaximizationSummary
• Previousresearchfocusesondesigningscalableandaccuratealgorithmforinfluencemaximization– Limitedbyinfluencespreadmodel– Limitedbyunknowninterpersonalinfluence[Wangetal.,AAAI2015]
• Forfurtherresearchoninfluencemaximization– Fullydata-driven
• e.g.,selectseednodesfromhistoricalcascades
– Withoutrequiringinfluencespreadmodel• End-to-endwithoutcaringthespreadpathofinfluence
PartIII:
Collectivecreditallocationinscience[Shenetal.,PNAS2014]
Creditallocation
Multi-authorpapersaredominatingthepublicationofscience,increasingby7percentevery10yearsbetween
1900and2012.
Science’screditsystemisunderpressuretoevolve:Thenormofcreditallocationforsingle-authorpublicationsfailsformulti-authorpublications.
Shen &Barabási,PNAS,2014
CreditallocationVOLUME 76, NUMBER 11 P HY S I CA L REV I EW LE T T ER S 11 MARCH 1996
Generation of Nonclassical Motional States of a Trapped Atom
D.M. Meekhof, C. Monroe, B. E. King, W.M. Itano, and D. J. WinelandTime and Frequency Division, National Institute of Standards and Technology, Boulder, Colorado 80303-3328
(Received 11 October 1995)We report the creation of thermal, Fock, coherent, and squeezed states of motion of a harmonically
bound 9Be1 ion. The last three states are coherently prepared from an ion which has been initiallylaser cooled to the zero point of motion. The ion is trapped in the regime where the coupling betweenits motional and internal states, due to applied (classical) radiation, can be described by a Jaynes-Cummings-type interaction. With this coupling, the evolution of the internal atomic state provides asignature of the number state distribution of the motion.
PACS numbers: 42.50.Vk, 32.80.Pj, 32.80.Qk
Nonclassical states of the harmonic oscillator associatedwith a single mode of the radiation field (for example,squeezed states) have been a subject of considerableinterest. One method for analyzing these states has beenthrough the dynamics of a single, two-level atom whichradiatively couples to the single mode radiation field.This system, described by the Jaynes-Cummings model(JCM) interaction [1,2], is important to the field of cavityQED [3].Nonclassical states of motion occur naturally on an
atomic scale, for example, for electrons in atoms and atomsin molecules. On a macroscopic scale, the benefits ofnonclassical mechanical states, such as squeezed states, fordetection of gravitational waves have been appreciated forsome time [4], but so far these states have not been realized.More recently, there has been interest in the generationand detection of nonclassical states of motion for an atomconfined in a macroscopic, harmonic trap; for trapped ions,see Refs. [5–16]. These states are of interest from thestandpoint of quantum measurement concepts and mayfacilitate other measurements such as sensitive detection[5,7,13] or quantum computation [17].In this Letter we report the generation and detection of
thermal, Fock, coherent, and squeezed states of motionof a single 9Be1 ion confined in an rf (Paul) trap. Wedetect the state of atomic motion by observing the evo-lution of the atom’s internal levels [6,11] (e.g., collapseand revival) under the influence of a JCM-type interactionrealized with the application of external (classical) fields.Under certain conditions, the interaction Hamiltonian isformally equivalent to the JCM Hamiltonian of cavityQED. Here, the harmonic motion of the atom replaces thesingle mode of the radiation field. The coupling can berealized by applying quasistatic fields [7], traveling-wavefields [6,10,13,15], or standing-wave laser fields [8,9,12].In each case the coupling H
I
≠ 2m ? Esrd between in-ternal and motional states is induced by the atom’s motionthrough the spatially inhomogeneous electromagnetic fieldEsrd, where m is the atomic dipole operator.In the present experiment, we drive stimulated Raman
transitions between two hyperfine ground states by apply-
ing a pair of traveling-wave laser beams detuned froman excited electronic state [18]. The resulting interactionbetween these internal states jSl (denoted j #l and j "l)and motional harmonic oscillator states jnl and jn0l in thex direction is given by matrix elements
kS0, n0jHI
jS, nl ≠ h̄VkS0, n
0js1e
ihsa1a
yd
1 s2e
2ihsa1a
ydjS, nl (1)in a frame which rotates at the difference frequency of thelaser beams. In this expression, s1 (s2) is the raising(lowering) operator for the internal atomic state, a
y (a) isthe harmonic oscillator raising (lowering) operator, and Vis the Raman coupling parameter [5,13,18]. The Lamb-Dicke parameter is defined by h ; dk x0, where dk isthe wave-vector difference of the two Raman beams alongx, and x0 ≠
ph̄y2mv is the spread of the jn ≠ 0l wave
function in the harmonic well of frequency v.The order n
0 2 n of the vibrational coupling is selectedby tuning the Raman beam difference frequency. For ex-ample, by tuning to the first red sideband in the Ramanspectrum, we resonantly enhance the term which drivestransitions between states j #, nl and j ", n 2 1l. In theLamb-Dicke limit [dk
pkx2l ø 1, x ≠ x0sa 1 a
yd], theexponentials in Eq. (1) can be expanded to lowest order,resulting in the operator hsas1 1 a
ys2d, which corre-sponds to the usual JCM operator. We can easily controlthe strength and duration of the interaction by varying theintensity and time the lasers are applied. By choosing otherlaser tunings, we can select other operators such as the anti-JCM operator hsays1 1 as2d at the first blue sideband(which is not present in cavity QED) or the “two-phonon”JCM operator sh2y2d sa2s1 1 a
y2s2d at the second redsideband. In this experiment, the higher-order terms inthe expansion of the exponential in Eq. (1) must also betaken into account [19]. Reference [20] has explicitly dis-cussed the consequences of these higher-order terms on thetrapped ion internal and motional state dynamics.Additional differences from cavity-QED experiments
include the methods of state generation available (de-scribed below) and the relatively small decoherence. Inall but the case of thermal states, we coherently prepare
1796
2012NobelPrize-winningpaperinPhysics
1997NobelPrize-winningpaperinPhysics
2007NobelPrize-winningpaperinPhysics
Shen &Barabási,PNAS,2014
Creditallocation1984NobelPrize-winningpaperinPhysics
Shen &Barabási,PNAS,2014
Alphabetic author list.
Creditallocation
Howtoallocatecreditformulti-authorpublications?
Problem:
Challenge:
1. Multipleauthorshipbreaksthesymmetrybetweenauthors’contributionandthecredittheygotfortheircontribution.
2. Itishardtoquantifytheactualcontributionofauthors,especiallyforthoseoutsideoftheparticularresearchfield.
3. Eachdisciplinerunsitsowninformalcreditallocationsystem.
Shen &Barabási,PNAS,2014
Creditallocation
CaseA2010NobelPrizeinChemistry
CaseB2010NobelPrizeinPhysics
Baba, Negishi,J.Am.Chem.Soc.98,6729(1976) Novoselov,Geim,Science,306,666(2004)
Shen &Barabási,PNAS,2014
Casestudy:
Frequentlyco-citedpapers:
1.Negishi,Okukado,King,Van Horn,Spiegel,J.Am.Chem.Soc.(1978)
2.Negishi,King,Okukado,J.Org.Chem.(1977)
3.Negishi,Vanhorn,J.Am.Chem.Soc.(1977)
4.Negishi,Vanhorn,J.Am.Chem.Soc.(1978)
5.Negishi,Valente.Kobayashi,J.Am.Chem.Soc.(1980)
Frequentlyco-citedpapers:
1.Geim,Novoselov,Nature(2007)
2.Novoselov,Jiang,Schedin,Booth,Khotkevich,Morozov,Geim,PNAS(2005)
3.Novoselov,Geim,Morozov,Jiang,Katsnelson,rigorieva,Dubonos,Firsov,Nature(2005)4.CastroNeto,Guinea,Peres,Novoselov,Geim,Rev.Mod.Phys.(2009)
5.Ferrari,Meyer,Scardaci,Casiraghi,Lazzeri,auri,Piscanec.Jiang,Novoselov,Roth,Geim.Phys.Rev.Lett.(2006)
Creditallocation
Co-cited papers:Co-citation strength sCredit allocation matrix A
Credit share:c=As
Shen &Barabási,PNAS,2014
Priorforcreditallocation
Anycreditallocationmethodforsinglemulti-authorpaperscouldbetakenasaprior,e.g.,fractionalcreditallocation,harmoniccreditallocation
Creditallocation
CaseA2010NobelPrizeinChemistry
CaseB2010NobelPrizeinPhysics
Baba, Negishi,J.Am.Chem.Soc.98,6729(1976) Novoselov,Geim,Science,306,666(2004)
Shen &Barabási,PNAS,2014
Caserevisiting:
Frequentlyco-citedpapers:
1.Negishi,Okukado,King,Van Horn,Spiegel,J.Am.Chem.Soc.(1978)
2.Negishi,King,Okukado,J.Org.Chem.(1977)
3.Negishi,Vanhorn,J.Am.Chem.Soc.(1977)
4.Negishi,Vanhorn,J.Am.Chem.Soc.(1978)
5.Negishi,Valente.Kobayashi,J.Am.Chem.Soc.(1980)
Frequentlyco-citedpapers:
1.Geim,Novoselov,Nature(2007)
2.Novoselov,Jiang,Schedin,Booth,Khotkevich,Morozov,Geim,PNAS(2005)
3.Novoselov,Geim,Morozov,Jiang,Katsnelson,rigorieva,Dubonos,Firsov,Nature(2005)4.CastroNeto,Guinea,Peres,Novoselov,Geim,Rev.Mod.Phys.(2009)
5.Ferrari,Meyer,Scardaci,Casiraghi,Lazzeri,auri,Piscanec.Jiang,Novoselov,Roth,Geim.Phys.Rev.Lett.(2006)
Creditshare:(0.28,0.72) Creditshare:(0.5,0.5)
Datasets• American Physical Society (APS)
– Period: 1893~2009– Papers and citations from all the 11 journals of APS– 463, 348 papers, 4, 710, 547 citations, and 248, 738 authors.
• Web of Science (WOS)– Period: 1955-2012– Multidisciplinary– 37, 553, 657 papers, 672, 321, 250 citations, and 8, 724, 394 authors
Creditallocation
Shen &Barabási,PNAS,2014
Datasets:APS: American Physical SocietyWOS: Web of science
Nobel prize-winning papers
Validation
Metric:Whether our method could identify the Nobel Laureates from the author list.
Wearecorrectat51of63testcases.Onehitsituation:Firstauthor:30;LastAuthor:32;Ourmethod:56
Middle-authorLaureates
CreditallocationCreditshareevolution
Shen &Barabási,PNAS,2014
ca: average credit share over 3 years after publication;cb: average credit share over 3 years before publication;Increase ratio: ca / cb
Tocheckcreditshareevolution,weonlythecitationsbeforeeachyearforcreditallocation
Nobelprizeeffect
CreditallocationComparingindependentauthors
Shen &Barabási,PNAS,2014
Three independent papers (six scientists) contribute to the discovery of Higgs Boson.
WhogetstheNobelprize,i.e.,whogetshighcreditfromtheNobelcommittee?
Creditallocation
"Ireallyratherhopedbeforetheannouncementthattheywouldmakethenumberuptothree,andtherewascertainlyanobviouscandidatetobethethird, TomKibble”
(PeterHiggs, BBCInterview2014)
Higgs
Kibble
Englert, Brout
Guralnik, Hagen
Comparingindependentauthors
Higgs & Englert
Kibble
• Wedevelopedamethodtoquantifythecreditshareofcoauthorsbyreproducingthecollectivecreditallocationprocess informallyusedbythescientificcommunity.– Creditisallocatedamongcoauthorsbasedontheirperceived
contributionratherthantheiractualcontribution;– Establishedscientistsreceivemorecreditthantheirjunior
collaboratorsfromtheircoauthoredpublication• Thissituationcanchange,however,ifthejunioronemakesimportantindependent
contributiontothefield
– Creditshareisadynamicquantitythechangeswiththeevolutionofthefield
CreditallocationSummary
• Creditisallocatedbythewholecommunity ratherthanthecoauthorsthemselves.
• Citation isthemostelementaryformofvisibilityandcreditinthescientificcommunity.– Othertokensofimpact,likeinvitedtalks,keynotes,mentoring,books,
implicitlyalterthecreditsharebyenhancingthevisibilityandcitationsrelativetoothercoauthors.
• Howtochoosethemostappropriatecollaborators?– Youknow.
CreditallocationWhatwecanlearnfromthis?
Doyouwanttoknowthecreditshareyougetfromyourpaper?shenhuawei@gmail.com
Acknowledgements
Xueqi Cheng
Suqi Cheng Yongqing WangPeng BaoJunming Huang
Albert-László Barabási
TongMan Bingjie Sun
Relatedpublicationsfromourgroup1. Hua-WeiShen,Albert-László Barabási.Collectivecreditallocationinscience.PNAS,111(34):
12325–12330,2014.2. HuaweiShen,Dashun Wang,Chaoming Song,Albert-László Barabási.Modelingand
predictingpopularitydynamicsviareinforcedPoissonprocess.AAAI2014.3. Dashun Wang,Chaoming Song,Hua-WeiShen,Albert-László Barabási.Responseto
Commenton“Quantifyinglong-termscientificimpact”.Science,345:149,2014.4. Jinhua Gao,HuaweiShen,Shenghua Liu,Xueqi Cheng.Modelingandpredictingretweeting
dynamicsviaamixtureprocess.WWW2016.5. Yongqing Wang,HuaweiShen,Shenghua Liu,Xueqi Cheng.Learninguser-specificlatent
influenceandsusceptibilityfrominformationcascades.AAAI2015.6. Suqi Cheng,HuaweiShen,JunmingHuang,WeiChen,Xueqi Cheng.IMRank:influence
maximizationviafindingself-consistentranking.SIGIR2014.7. Suqi Cheng,HuaweiShen,JunmingHuang,Guoqing Zhang,Xueqi Cheng.StaticGreedy:
solvingthescalability-accuracydilemmaininfluencemaximization,CIKM2013.8. Peng Bao,Hua-WeiShen,JunmingHuang,Xue-QiCheng.Popularitypredictionin
microbloggingnetwork:AcasestudyonSina weibo.WWW2013.9. TongMan,HuaweiShen,Shenghua Liu,Xiaolong Jin,Xueqi Cheng.Predictanchorlinks
acrosssocialnetworksviaanembeddingapproach.IJCAI201610. Hao Wang,Hua-WeiShen*,Xue-QiCheng.Scientificcreditdiffusion:Researcherlevelor
paperlevel?Scientometrics,2016.
Huawei [email protected]
Thankyou!