高效的时间图学习：算法、框架与工具 Towards Efficient Temporal Graph Learning-Algorithms,Frameworks,and Tools

TowardsEfficientTemporalGraphLearning:

Algorithms,Frameworks,andTools

RuijieWangWanyuZhaoDachunSunCharithMendisTarekAbdelzaher

UniversityofIllinoisUrbana-Champaign

{ruijiew2,wanyu2,dsun18,charithm,zaher}@I

Time:1:45PM-17:30PM,October21,2024

Location:Room120C,BoiseCentre,Boise,ID

Webpage:

https://wjerry5.github.io/cikm2024-tutorial/

Contents

•PartI-Introduction

•PartII-Data-EfficientTemporalGraphNeuralNetwork

•30-minCoffeeBreak

•PartIII-Resource-EfficientTemporalGraphNeuralNetwork

•PartIV-DiscussionandFutureDirections

3

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

BroadApplicationDomainsofGraphData

SocialNetworkAnalysisKnowledgeGraphReasoningWebMining

RecommendationScientificDiscoveryLLMPrompting&Reasoning

Universallanguagefordescribinginterconnecteddata!

4

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Real-WorldGraphsareEvolving–TemporalGraphs

TemporalFactsinKGs

MolecularDynamics

UserOnlineBehaviors

DynamicalSystems

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Real-WorldGraphsareEvolving–TemporalGraphs

oGraphshavetime-evolvingcomponents,e.g.,

oTopologystructures

oAdd/deletenodes

oAdd/deleteedges

oInputfeatures

oNode-levelfeatures

oEdge-levelfeatureso…

Dynamicedges[1]Dynamicnodeset[2]

Dynamicnode&edgefeatures[3]

[1]

/temporal-graph-networks-ab8f327f2efe

.

[2]Wanget.al.,LearningtoSampleandAggregate:Few-shotReasoningoverTemporalKnowledgeGraphs.

[3]ThomasKipf,EthanFetaya,Kuan-ChiehWang,MaxWelling,andRichardZemel.Neuralrelationalinferenceforinteractingsystems.

5

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

TemporalGraphs–Definition

oDiscrete-timevs.continuous-timetemporalgraphs

oDiscrete-timetemporalgraphs

oG={G1,…,GT−1,GT},

owhereGt=(ℰt,vt,xt)denotesone

snapshot.Discrete-timeexample[1]

oContinuous-timetemporalgraphs

oG={(ei,ej,t,+/−)},

owhereei,ej∈ℰ,0≤t<T

Continuous-timeexample[2]

Howtoenabledeeplearningontemporalgraphs?

[1]Fuet.al.,NaturalandArtificialDynamicsinGNNs.

[2]Conget.al.,OntheGeneralizationCapabilityofTemporalGraphLearningAlgorithms:TheoreticalInsightsandaSimplerMethod.

6

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

GraphNeuralNetworks(GNNs)

oNodeshaverepresentationsateachlayer,wherelayer-0representationsareinputfeaturesx.

oBasicoperations:Sample&Aggregation+Update

Step1:Sample&Aggregate

Combinemsgsfromneighbors

Step2:Update

[1]WilliamL.HamiltonandJianTang.“GraphRepresentationLearning”.TutorialatAAAI2019.

Applyneuralnetworks

7

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

TemporalGraphNeuralNetworks(TGNNs)

oNodeshaverepresentationsateachlayer,wherelayer-0representationsareinputfeaturesx.

oNewoperationdesigns:Sample&Aggregation+Update

Step1:Sample&Aggregate

Combinemsgsfromneighbors

8

Step2:Update

Applyneuralnetworks

[1]WilliamL.HamiltonandJianTang.“GraphRepresentationLearning”.TutorialatAAAI2019.

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

TemporalGraphNeuralNetworks(TGNNs)

oCategoriesofTGNNs

oTGNNwithRNN

oTGNNwithselfattention

JODIE[1]

DySAT[2]

oTGNNwithmemory

TGAT[3]

oTGNNwithmemory&selfattention

o……

TGN[4]

[1]Kumaretal.,JODIE:PredictingDynamicEmb.TrajectoryinTemporalInteractionNetworks.[2]Sankaretal.,DySAT:DeepNeuralRepr.LearningonDynamicGraphsviaSelf-Attention

Networks.

[3]Xuetal.,InductiveRepresentationLearningonTemporalGraphs

[4]Rossietal.,TemporalGraphNetworksforDeepLearningonDynamicGraphs

9

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

TrainingandinferencepipelineofTGNNs

Evaluation

InputGraph

TGNNs

PredictionHead

Predictions

Labels

Loss

Function

10

oRepresentationlearning+task-relatedoptimization.

Time-EvolvingEmbeddings

[1]Congetal.,DoWeReallyNeedComplicatedModelArchitecturesForTemporalNetworks?

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Data-EfficiencyIssueofTGNNs

Evaluation

TGNNs

Time-EvolvingEmbeddings

PredictionHead

Predictions

Labels

Loss

Function

oTrainingTGNNsrequiresrelativelyabundantlabeleddata.

InputGraph

oInsufficientlabeleddataforreal-worldapplications:

oIndirectlabels

oScarcityoftask-specificlabels

oLimitedlabelsfornewtasks/distributions

11

12

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

ComputationpipelineofTGNNs

oTrainingcomputation

FeatureFetching

Model

Computation

FeatureUpdate

Inference

oInferencecomputation

NeighborSampling

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Resource-EfficiencyIssueofTGNNs

NeighborSampling

Model

Computation

FeatureUpdate

Inference

oFastgrowingoftemporalgraphsv.s.limitedresources

FeatureFetching

13

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

ScopeofThisTutorial

PartII

Data-EfficientTGNN

EfficientTGNN

PartIII

Resource-EfficientTGNN

oWefocusonalgorithm

designandoptimization

techniquestoaddressthechallengesposedby

insufficientlabels.

14

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

ScopeofThisTutorial

EfficientTGNN

PartII

Data-EfficientTGNN

PartIII

Resource-EfficientTGNN

Self-SupervisedLearning

Weakly-SupervisedLearning

Few-ShotLearning

15

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

ScopeofThisTutorial

EfficientTGNN

PartII

Data-EfficientTGNN

PartIII

Resource-EfficientTGNN

Self-SupervisedLearning

oWefocusonsystem

Weakly-SupervisedLearning

accelerationtoenablelarge-scaletrainingandinferencewithlimited

Few-ShotLearning

resources.

16

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

ScopeofThisTutorial

EfficientTGNN

PartII

Data-EfficientTGNN

PartIII

Resource-EfficientTGNN

Self-SupervisedLearning

TrainingAcceleration

Weakly-SupervisedLearning

InferenceAcceleration

Few-ShotLearning

DistributedTrainingAcceleration

17

Contents

•PartI-Introduction

•PartII-Data-EfficientTemporalGraphNeuralNetwork

•30-minCoffeeBreak(15:30-16:00)

•PartIII-Resource-EfficientTemporalGraphNeuralNetwork

•PartIV-DiscussionandFutureDirections

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

ScopeofThisTutorial

EfficientTGNN

PartII

Data-EfficientTGNN

PartIII

Resource-EfficientTGNN

TrainingAcceleration

Self-SupervisedLearning

InferenceAcceleration

Weakly-SupervisedLearning

DistributedTrainingAcceleration

Few-ShotLearning

19

20

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Self-SupervisedLearningonTemporalGraphs

•Introduction&Background

•Self-SupervisionbyReconstruction

•Self-SupervisionbyContrastiveApproach

•Self-SupervisionbyMultiviewConsistency

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Introduction–Self-SupervisedLearning(SSL)

oLearningusefulrepresentationswithoutrequiringlabeleddata.

oReliesontheinherentstructureandtemporaldynamicsofthegraphitself.

InductiveTask

Examineinferredrepresentationsofunseennodeby

predictingthefuturelinksbetweenunseennodesand

classifythembasedontheirinferredembedding

dynamically

TransductiveTask

Examinenodeembeddingsthathavebeenobservedin

training,viathefuturelinkpredictiontaskandthenode

classification.

t1t2tn

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PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

ChallengesonTemporalGraphs

oChallenge1:Nodeembeddingsshouldalsofunctionoftime.

oChallenge2:Temporalconstraintsonneighborhoodaggregationmethods.

oChallenge3:Possiblymultiplenodeinteractions.

[1]DaXu,ChuanweiRuan,EvrenKorpeoglu,SushantKumar,KannanAchan,InductiveRepresentationLearningonTemporalGraphs22

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Background–AttentionMechanismonGraphs

oQueryneighborsbykeysderivedfromtheirrepresentations,aggregatingtheir

valuebytheattentionweight.

oQuestion:Howtoinvolvetemporalinformation?

PetarVeličković,GuillemCucurull,ArantxaCasanova,AdrianaRomero,PietroLiò,YoshuaBengio,GraphAttentionNetworks23

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Background–TimeEncoding

oAformof“positionalencoding”concatenatedtothenoderepresentation.

oGenerateavectorencodinggivenarealnumber.

oEncodingrepresentstimespanratherthanabsolutevalueoftime(Translation-invariance).

K(t1,ta):=(I(t1),J(ta)》K(t1+c,tz+c)=k(t1,ta)

UsingBochner’sTheoremandMonteCarloapproximation:

24

K(t,ta)~⃞工:1cos(urt1)cos(orta)+sin(urt1)sin(urta)

[1]DaXu,ChuanweiRuan,EvrenKorpeoglu,SushantKumar,KannanAchan,InductiveRepresentationLearningonTemporalGraphs

26

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Background–TemporalSubgraphSampling

oTemporalsubgraphsamplingiskeytobatch-wisetrainingandcontrastivepairconstruction.

oMessagepassingdirectionsmustalignwiththeobservedchronologicalorders.

oGivenatargetnumberofnodesforsubgraph,candidatescanbefurtherweightedby

structuralortemporalimportance.

oDegree,centrality,orPageRank

oTimeelapsed.

27

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Introduction–Self-SupervisedLearning(SSL)

oSSLparadigmstypicallygeneratesupervisionsignalsthroughdesignedtasks:

oTransductivefuturelinkreconstruction.Lossisbasedoncrossentropy.

oContrastivelearning:learningfrompositiveandnegativepairofexamples.Lossisbasedonsimilaritymeasure.

oMultiviewconsistency:representationsshouldberobustunderperturbationsandagreewitheachother.Lossisbasedonregularizations.

NewBatch

EdgeProbabilities

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyReconstruction:TGAT

oObjective:Producetime-awarerepresentationforatargetnodeattimepointt.

oMotivation:AnalogoustoGraphSAGEorGAT,takestemporalneighborhoodwithhiddenrepresentationsandtimestamps,andaggregate.

oMethod:Alocalaggregationoperator,usingattentionmechanism.

0

oLink

prediction

loss:

28

[1]DaXu,ChuanweiRuan,EvrenKorpeoglu,SushantKumar,KannanAchan,InductiveRepresentationLearningonTemporalGraphs

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyReconstruction:TGAT

oExperiments:Transductive&inductivelearningtaskforfuturelinkprediction.

[1]DaXu,ChuanweiRuan,EvrenKorpeoglu,SushantKumar,KannanAchan,InductiveRepresentationLearningonTemporalGraphs29

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyReconstruction:TemporalGraphNetworks(TGN)

oMotivation:Viewingdynamicgraphsassequencesoftimedevents.

oMethod:Fivemodulesthatprocessdynamicgraphsasaseriesofnode-wise

event,interactionevent,ordeletionevent,andsavethenodestatestomemory.

Aggregatedmessages

Rawmessages

Messages

Memory

m:(t)=msgs(s,(t"),sj(t"),At,ej(t))

Identity,MLP

Mostrecent,Mean

LSTM,GRU

[1]EmanueleRossi,BenChamberlain,FabrizioFrasca,DavideEynard,FedericoMonti,MichaelBronstein,TemporalGraphNetworksforDeepLearningonDynamicGraphs30

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyReconstruction:TemporalGraphNetworks(TGN)

oMotivation:Viewingdynamicgraphsassequencesoftimedevents.

oMethod:Fivemodulesthatprocessdynamicgraphsasaseriesofnode-wise

event,interactionevent,ordeletionevent,andsavethenodestatestomemory.

NewBatch

MemoryNodeEmbeddingsEdgeProbabilities

Identity,

TimeProjection

TemporalGraphAttention

31

TemporalGraphSum

[1]EmanueleRossi,BenChamberlain,FabrizioFrasca,DavideEynard,FedericoMonti,MichaelBronstein,TemporalGraphNetworksforDeepLearningonDynamicGraphs

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyReconstruction:TemporalGraphNetworks(TGN)

oExperiments:Transductive&inductivelearningtaskforfuturelinkprediction.

[1]EmanueleRossi,BenChamberlain,FabrizioFrasca,DavideEynard,FedericoMonti,MichaelBronstein,TemporalGraphNetworksforDeepLearningonDynamicGraphs32

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyContrastiveLearning:TGAT-CL

oMotivation:Noderepresentationprocessisingeneral“smooth”.

oMethod:Contrastthesamenoderepresentationovertime.

Q(tx,ty)=S(Itx-tyl)

[1]ShengTian,RuofanWu,LeileiShi,LiangZhu,TaoXiong,Self-supervisedRepresentationLearningonDynamicGraphs33

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyContrastiveLearning:TGAT-CL

oMotivation:Noderepresentationprocessisingeneral“smooth”.

oMethod:Contrastthesamenoderepresentationovertime.

oChallenge:Biasinnegativeexamplesampling.

sim(x,y)=x'yS(tx,ty)=s(Itx-tyl)

oContrastiveLoss

[1]ShengTian,RuofanWu,LeileiShi,LiangZhu,TaoXiong,Self-supervisedRepresentationLearningonDynamicGraphs34

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyContrastiveLearning:TGAT-CL

oMotivation:Noderepresentationprocessisingeneral“smooth”.

oMethod:Contrastthesamenoderepresentationovertime.

oChallenge:Biasinnegativeexamplesampling.

oDebiasedContrastiveLoss:

tt=p(c(x')=c(x))

[1]ShengTian,RuofanWu,LeileiShi,LiangZhu,TaoXiong,Self-supervisedRepresentationLearningonDynamicGraphs35

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyContrastiveLearning:TGAT-CL

oMotivation:DynamicnodeclassificationperformanceinaverageAUCanddynamiclinkpredictionperformanceinaverageprecision.

[1]ShengTian,RuofanWu,LeileiShi,LiangZhu,TaoXiong,Self-supervisedRepresentationLearningonDynamicGraphs36

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyContrastiveLearning:DySubC

oMotivation:Nodevs.subgraphrepresentations;temporalvs.staticrepresentations.

oMethod:Contrastbetweenapositivesubgraphsample,atemporalnegativesample,andastructuralnegativesample.

Readout:

[1]Ke-JiaChen,LinsongLiu,LinpuJiang,JingqiangChen,Self-SupervisedDynamicGraphRepresentationLearningviaTemporalSubgraphContrast37

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyContrastiveLearning:DySubC

oMotivation:Nodevs.subgraphrepresentations;temporalvs.staticrepresentations.

oMethod:Contrastbetweenapositivesubgraphsample,atemporalnegativesample,andastructuralnegativesample.

TrainingLoss:

L=L1+LZ

[1]Ke-JiaChen,LinsongLiu,LinpuJiang,JingqiangChen,Self-SupervisedDynamicGraphRepresentationLearningviaTemporalSubgraphContrast38

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyContrastiveLearning:DySubC

oExperiments:LinkpredictionintermsofaverageAUCscoreandAccuracy.

[1]Ke-JiaChen,LinsongLiu,LinpuJiang,JingqiangChen,Self-SupervisedDynamicGraphRepresentationLearningviaTemporalSubgraphContrast39

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyMultiviewConsistency:DyG2Vec

oMotivation:Embeddingsshouldbeconsistentundergraphperturbations.

oNewGraphEncoder:Accordingtoablationstudy,thesubgraphencoderisundirectedandnon-causal.

[1]MohammadAlomrani,MahdiBiparva,YingxueZhang,MarkCoates,DyG2Vec:EfficientRepresentationLearningforDynamicGraphs40

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyMultiviewConsistency:DyG2Vec

oMotivation:Embeddingsshouldbeconsistentundergraphaugmentations.

oMethod:Useedgedropoutandedgefeaturemaskingtoproducedifferent“views”,anduseregularization-basedSSLlossfunction

41

+viciz)+ciz'l

[1]MohammadAlomrani,MahdiBiparva,YingxueZhang,MarkCoates,DyG2Vec:EfficientRepresentationLearningforDynamicGraphs

[2]AdrienBardes,JeanPonce,YannLeCun,VICReg:Variance-Invariance-CovarianceRegularizationforSelf-SupervisedLearning

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

SSLbyMultiviewConsistency:DyG2Vec

oExperiments:LinkpredictionintermsofaverageAUCscoreandAccuracy.

[1]MohammadAlomrani,MahdiBiparva,YingxueZhang,MarkCoates,DyG2Vec:EfficientRepresentationLearningforDynamicGraphs42

43

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Data-EfficientTGNNCheckpoint

Self-SupervisedLearning

oIntroduction&Background

oReconstruction

oContrastiveLearning

oMultiviewApproach

Q&A

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

ScopeofThisTutorial

EfficientTGNN

PartII

Data-EfficientTGNN

PartIII

Resource-EfficientTGNN

Self-SupervisedLearning

TrainingAcceleration

InferenceAcceleration

Weakly-SupervisedLearning

Few-ShotLearning

DistributedTrainingAcceleration

44

45

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Weakly-SupervisedLearningonTemporalGraphs

•Introduction&Background

•Weak-SupervisionwithLimitedInformation

•Weak-SupervisiononSparseTemporalGraph

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Introduction–Weakly-SupervisedLearning

oLearninguseful(orbetter)representationsusinglimitedlabeledornoisydata.

oInherentstructureandtemporaldynamicsofthegraphitselfarestillimportant.

oChallenge1:Effectivelyexploitweakinformationinthetrainingprocess.

oChallenge2:Learningrepresentationsondynamicandnoisygraphs.

[1]Leftimageisfrom

/watch?v=WQb6h19PrJA

[2]LinhaoLuo,GholamrezaHaffari,ShiruiPan,GraphSequentialNeuralODEProcessforLinkPredictiononDynamicandSparseGraphs

46

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Weak-SupervisiononLimitedLabeledData:D2PT

oMotivation:DesignauniversalandeffectiveGNNforgraphlearningwithweakinformation(GLWI).Disclaimer:Thisworkisforstaticgraphs.

oMethod:ExecuteeffectiveinformationpropagationinGNNs.

[1]YixinLiu,KaizeDing,JianlingWang,VincentLee,HuanLiu,ShiruiPan,LearningStrongGraphNeuralNetworkswithWeakInformation47

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Weak-SupervisiononLimitedLabeledData:D2PT

oMotivation:DesignauniversalandeffectiveGNNforgraphlearningwithweakinformation(GLWI).Disclaimer:Thisworkisforstaticgraphs.

oMethod:ExecuteeffectiveinformationpropagationinGNNs.

[1]YixinLiu,KaizeDing,JianlingWang,VincentLee,HuanLiu,ShiruiPan,LearningStrongGraphNeuralNetworkswithWeakInformation48

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Weak-SupervisiononLimitedLabeledData:D2PT

oExperiments:ClassificationaccuracyinextremeGLWIscenario.

[1]YixinLiu,KaizeDing,JianlingWang,VincentLee,HuanLiu,ShiruiPan,LearningStrongGraphNeuralNetworkswithWeakInformation49

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Weak-SupervisiononSparseTemporalGraph:GSNOP

oObjective:Addressthesituationwherethereisnotenoughhistoricaldata.

oMotivation:Missinglinksarecommon.Howtolearnbetterrepresentationonsparsegraphsandpreventoverfitting.

[1]LinhaoLuo,GholamrezaHaffari,ShiruiPan,GraphSequentialNeuralODEProcessforLinkPredictiononDynamicandSparseGraphs50

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Weak-SupervisiononSparseTemporalGraph:GSNOP

oMethod:Treatthelinkpredictionasadynamic-changingstochasticprocessandemployneuralprocess.

[1]LinhaoLuo,GholamrezaHaffari,ShiruiPan,GraphSequentialNeuralODEProcessforLinkPredictiononDynamicandSparseGraphs51

PartI-IntroductionPartII-Data-EfficientTGNNPartIII-Resource-EfficientTGNNPartIV-Discussion&Future

Weak-SupervisiononSparseTemporalGraph:GSNOP

oMethod:Treatthelinkpredictionasadynamic-changingstochasticprocessandemployneuralprocess.

[1]LinhaoLuo,GholamrezaHaffari,ShiruiPan,GraphSequentialNeuralODEProcessforLinkPredictiononDynamicandSparseGraphs52

PartI-Introduction