Table Of ContentSocial Network Data Analytics
Charu C. Aggarwal
Editor
Social Network
Data Analytics
1 C
Editor
Charu C. Aggarwal
IBM Thomas J. Watson Research Center
19 Skyline Drive
Hawthorne, NY 10532, USA
[email protected]
ISBN 978-1-4419-8461-6 e-ISBN 978-1-4419-8462-3
DOI 10.1007/978-1-4419-8462-3
Springer New York Dordrecht Heidelberg London
Library of Congress Control Number: 2011922836
© Springer Science+Business Media, LLC 2011
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Contents
Preface xiii
1
AnIntroductiontoSocialNetworkDataAnalytics 1
CharuC.Aggarwal
1. Introduction 1
2. OnlineSocialNetworks:ResearchIssues 5
3. ResearchTopicsinSocialNetworks 8
4. ConclusionsandFutureDirections 13
References 14
2
StatisticalPropertiesof 17
SocialNetworks
MaryMcGlohon,LemanAkogluandChristosFaloutsos
1. Preliminaries 19
1.1 De(cid:191)nitions 19
1.2 Datadescription 24
2. StaticProperties 26
2.1 StaticUnweightedGraphs 26
2.2 StaticWeightedGraphs 27
3. DynamicProperties 32
3.1 DynamicUnweightedGraphs 32
3.2 DynamicWeightedGraphs 36
4. Conclusion 39
References 40
3
RandomWalksinSocialNetworksandtheirApplications:ASurvey 43
PurnamritaSarkarandAndrewW.Moore
1. Introduction 43
2. RandomWalksonGraphs:Background 45
2.1 RandomWalkbasedProximityMeasures 46
2.2 OtherGraph-basedProximityMeasures 52
2.3 Graph-theoreticMeasuresforSemi-supervisedLearning 53
2.4 Clusteringwithrandomwalkbasedmeasures 56
3. RelatedWork:Algorithms 57
3.1 AlgorithmsforHittingandCommuteTimes 58
3.2 AlgorithmsforComputingPersonalizedPagerankandSim-
rank 60
vi SOCIALNETWORKDATAANALYTICS
3.3 AlgorithmsforComputingHarmonicFunctions 63
4. RelatedWork:Applications 63
4.1 ApplicationinComputerVision 64
4.2 TextAnalysis 64
4.3 CollaborativeFiltering 65
4.4 CombatingWebspam 66
5. RelatedWork:Evaluationanddatasets 66
5.1 Evaluation:LinkPrediction 66
5.2 PubliclyAvailableDataSources 68
6. ConclusionandFutureWork 69
References 71
4
CommunityDiscoveryinSocial 79
Networks:Applications,Methods
andEmergingTrends
S.Parthasarathy,Y.RuanandV.Satuluri
1. Introduction 80
2. CommunitiesinContext 82
3. CoreMethods 84
3.1 QualityFunctions 85
3.2 TheKernighan-Lin(KL)algorithm 86
3.3 Agglomerative/DivisiveAlgorithms 87
3.4 SpectralAlgorithms 89
3.5 Multi-levelGraphPartitioning 90
3.6 MarkovClustering 91
3.7 OtherApproaches 92
4. EmergingFieldsandProblems 95
4.1 CommunityDiscoveryinDynamicNetworks 95
4.2 CommunityDiscoveryinHeterogeneousNetworks 97
4.3 CommunityDiscoveryinDirectedNetworks 98
4.4 Coupling Content and Relationship Information for Com-
munityDiscovery 100
5. CrosscuttingIssuesandConcludingRemarks 102
References 104
5
NodeClassi(cid:191)cationinSocialNetworks 115
SmritiBhagat,GrahamCormodeandS.Muthukrishnan
1. Introduction 116
2. ProblemFormulation 119
2.1 Representingdataasagraph 119
2.2 TheNodeClassi(cid:191)cationProblem 123
3. MethodsusingLocalClassi(cid:191)ers 124
3.1 IterativeClassi(cid:191)cationMethod 125
4. RandomWalkbasedMethods 127
4.1 LabelPropagation 129
4.2 GraphRegularization 132
4.3 Adsorption 134
5. ApplyingNodeClassi(cid:191)cationtoLargeSocialNetworks 136
5.1 BasicApproaches 137
5.2 Second-orderMethods 137
5.3 ImplementationwithinMap-Reduce 138
Contents vii
6. Relatedapproaches 139
6.1 InferenceusingGraphicalModels 139
6.2 Metriclabeling 140
6.3 SpectralPartitioning 141
6.4 GraphClustering 142
7. VariationsonNodeClassi(cid:191)cation 142
7.1 DissimilarityinLabels 142
7.2 EdgeLabeling 143
7.3 LabelSummarization 144
8. ConcludingRemarks 144
8.1 FutureDirectionsandChallenges 145
8.2 FurtherReading 146
References 146
6
EvolutioninSocialNetworks:ASurvey 149
MyraSpiliopoulou
1. Introduction 149
2. Framework 151
2.1 ModelingaNetworkacrosstheTimeAxis 151
2.2 EvolutionacrossFourDimensions 152
3. ChallengesofSocialNetworkStreams 154
4. IncrementalMiningforCommunityTracing 156
5. TracingSmoothlyEvolvingCommunities 160
5.1 TemporalSmoothnessforClusters 160
5.2 DynamicProbabilisticModels 162
6. LawsofEvolutioninSocialNetworks 167
7. Conclusion 169
References 170
7
ASurveyofModelsandAlgorithmsforSocialIn(cid:192)uenceAnalysis 177
JimengSunandJieTang
1. Introduction 177
2. In(cid:192)uenceRelatedStatistics 178
2.1 EdgeMeasures 178
2.2 NodeMeasures 180
3. SocialSimilarityandIn(cid:192)uence 183
3.1 Homophily 183
3.2 ExistentialTestforSocialIn(cid:192)uence 188
3.3 In(cid:192)uenceandActions 189
3.4 In(cid:192)uenceandInteraction 195
4. In(cid:192)uenceMaximizationinViralMarketing 200
4.1 In(cid:192)uenceMaximization 200
4.2 OtherApplications 206
5. Conclusion 208
References 209
8
ASurveyofAlgorithmsandSystemsforExpertLocationinSocialNetworks 215
TheodorosLappas,KunLiuandEvimariaTerzi
1. Introduction 216
viii SOCIALNETWORKDATAANALYTICS
2. De(cid:191)nitionsandNotation 217
3. ExpertLocationwithoutGraphConstraints 219
3.1 LanguageModelsforDocumentInformationRetrieval 219
3.2 LanguageModelsforExpertLocation 220
3.3 FurtherReading 221
4. ExpertLocationwithScorePropagation 221
4.1 ThePageRankAlgorithm 222
4.2 HITSAlgorithm 223
4.3 ExpertScorePropagation 224
4.4 FurtherReading 226
5. ExpertTeamFormation 227
5.1 Metrics 227
5.2 FormingTeamsofExperts 228
5.3 FurtherReading 232
6. OtherRelatedApproaches 232
6.1 Agent-basedApproach 233
6.2 In(cid:192)uenceMaximization 233
7. ExpertLocationSystems 235
8. Conclusions 235
References 236
9
ASurveyofLinkPrediction 243
inSocialNetworks
MohammadAlHasanandMohammedJ.Zaki
1. Introduction 244
2. Background 245
3. FeaturebasedLinkPrediction 246
3.1 FeatureSetConstruction 247
3.2 Classi(cid:191)cationModels 253
4. BayesianProbabilisticModels 259
4.1 LinkPredictionbyLocalProbabilisticModels 259
4.2 NetworkEvolutionbasedProbabilisticModel 261
4.3 HierarchicalProbabilisticModel 263
5. ProbabilisticRelationalModels 264
5.1 RelationalBayesianNetwork 266
5.2 RelationalMarkovNetwork 266
6. LinearAlgebraicMethods 267
7. RecentdevelopmentandFutureWorks 269
References 270
10
PrivacyinSocialNetworks:ASurvey 277
ElenaZhelevaandLiseGetoor
1. Introduction 277
2. Privacybreachesinsocialnetworks 280
2.1 Identitydisclosure 281
2.2 Attributedisclosure 282
2.3 Sociallinkdisclosure 283
2.4 Af(cid:191)liationlinkdisclosure 284
3. Privacyde(cid:191)nitionsforpublishingdata 286
k
3.1 -anonymity 288
Contents ix
l t
3.2 -diversityand -closeness 290
3.3 Differentialprivacy 291
4. Privacy-preservingmechanisms 292
4.1 Privacymechanismsforsocialnetworks 292
4.2 Privacymechanismsforaf(cid:191)liationnetworks 297
4.3 Privacymechanismsforsocialandaf(cid:191)liationnetworks 300
5. Relatedliterature 302
6. Conclusion 302
References 303
11
VisualizingSocialNetworks 307
CarlosD.CorreaandKwan-LiuMa
1. Introduction 307
2. ATaxonomyofVisualizations 309
2.1 StructuralVisualization 309
2.2 SemanticandTemporalVisualization 313
2.3 StatisticalVisualization 315
3. TheConvergenceofVisualization,InteractionandAnalytics 316
3.1 StructuralandSemanticFilteringwithOntologies 319
3.2 Centrality-basedVisualDiscoveryandExploration 319
4. Summary 322
References 323
12
DataMininginSocialMedia 327
GeoffreyBarbierandHuanLiu
1. Introduction 327
2. DataMininginaNutshell 328
3. SocialMedia 330
4. MotivationsforDataMininginSocialMedia 332
5. DataMiningMethodsforSocialMedia 333
5.1 DataRepresentation 334
5.2 DataMining-AProcess 335
5.3 SocialNetworkingSites: IllustrativeExamples 336
5.4 TheBlogosphere:IllustrativeExamples 340
6. RelatedEfforts 344
6.1 EthnographyandNetnography 344
6.2 EventMaps 345
7. Conclusions 345
References 347
13
TextMininginSocialNetworks 353
CharuC.AggarwalandHaixunWang
1. Introduction 354
2. KeywordSearch 356
2.1 QuerySemanticsandAnswerRanking 357
2.2 KeywordsearchoverXMLandrelationaldata 358
2.3 Keywordsearchovergraphdata 360
3. Classi(cid:191)cationAlgorithms 366
x SOCIALNETWORKDATAANALYTICS
4. ClusteringAlgorithms 369
5. TransferLearninginHeterogeneousNetworks 371
6. ConclusionsandSummary 373
References 374
14
IntegratingSensorsandSocialNetworks 379
CharuC.AggarwalandTarekAbdelzaher
1. Introduction 379
2. SensorsandSocialNetworks:TechnologicalEnablers 383
3. DynamicModelingofSocialNetworks 385
4. SystemDesignandArchitecturalChallenges 387
4.1 Privacy-preservingdatacollection 388
4.2 GeneralizedModelConstruction 389
4.3 Real-timeDecisionServices 389
4.4 RecruitmentIssues 390
4.5 OtherArchitecturalChallenges 390
5. DatabaseRepresentation:IssuesandChallenges 391
6. PrivacyIssues 399
7. SensorsandSocialNetworks:Applications 402
7.1 TheGoogleLatitudeApplication 402
7.2 TheCitysenseandMacrosenseApplications 403
7.3 GreenGPS 404
7.4 MicrosoftSensorMap 405
7.5 AnimalandObjectTrackingApplications 405
7.6 ParticipatorySensingforReal-TimeServices 406
8. FutureChallengesandResearchDirections 407
References 407
15
MultimediaInformationNetworks 413
inSocialMedia
LiangliangCao,GuoJunQi,Shen-FuTsai,Min-HsuanTsai,AndreyDelPozo,Thomas
S.Huang,XuemeiZhangandSukHwanLim
1. Introduction 414
2. LinksfromSemantics:Ontology-basedLearning 415
3. LinksfromCommunityMedia 416
3.1 RetrievalSystemsforCommunityMedia 417
3.2 RecommendationSystemsforCommunityMedia 418
4. NetworkofPersonalPhotoAlbums 420
4.1 Actor-CentricNatureofPersonalCollections 420
4.2 QualityIssuesinPersonalCollections 421
4.3 TimeandLocationThemesinPersonalCollections 422
4.4 ContentOverlapinPersonalCollections 422
5. NetworkofGeographicalInformation 423
5.1 SemanticAnnotation 425
5.2 GeographicalEstimation 425
5.3 OtherApplications 426
6. InferenceMethods 427
6.1 Discriminativevs.GenerativeModels 427
6.2 Graph-basedInference:Ranking,ClusteringandSemi-supervised
Learning 428
Description:Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive stream
