Behavioral Analysis and Prediction in Social Networks · (Full paper) • Peng Cui, Fei Wang,...
Transcript of Behavioral Analysis and Prediction in Social Networks · (Full paper) • Peng Cui, Fei Wang,...
Behavioral Analysis and Behavioral Analysis and Prediction in Social NetworksPrediction in Social Networks
Peng Cui Peng Cui 崔崔鹏鹏Media and Networking LabMedia and Networking Lab
Department of Computer ScienceTsinghua Universityg y
OutlineOutline
• Research works overview• Behavioral Analysis and Prediction on SocialBehavioral Analysis and Prediction on Social Networks
OutlineOutline
• Research works overview• Behavioral Analysis and Prediction on SocialBehavioral Analysis and Prediction on Social Networks
Research TopicsResearch TopicsSocial Social
InfluenceInfluence
2QuantifyQuantify
Influence Influence A&PA&P
ll
1 3Rich Rich
contextscontextsHot and Hot and trendingtrending
Topic Topic DiscoveryDiscovery
Social Social RecommRecomm‐‐
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Social Social
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MediaMedia46 Physical Physical
locationallocationalBeyond Beyond object labelsobject labels
MobileMobileMultimeMultimediadia
5
object labelsobject labels
Diffusion Diffusion DynamicDynamicSpreading Spreading
patternspatterns
Research TopicsResearch TopicsSocial Social
InfluenceInfluence
2
Influence Influence A&PA&P
ll
1 3
Papers:Topic Topic
DiscoveryDiscovery
Social Social RecommRecomm‐‐
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Papers:CIKM’12, ACM MM’12, SIGIR’11, AAAI’11, CIKM’10 DASFAA’10
Social Social
endendCIKM 10, DASFAA 10, ICDM’08, WWW’07DMKD, IEEE T‐MM,
NetworkNetwork46
IEEE T‐IP
Projects:MobileMobileSocial Social
MediaMedia5
Projects:GoogleTencentSamsung
Diffusion Diffusion
SamsungNSFCEtc.
DynamicDynamic
Social Recommendation SystemSocial Recommendation System
Providing personal recommendations onfriends friends
informationinformationcommunitiescommunities
Topic Category BrowsingTopic Category BrowsingFinding the topic categories from theFinding the topic categories from the received web posts.
Friends RankingFriends RankingRanking the close friends according to interaction patterns.
Community Trending TopicsCommunity Trending TopicsCommunity Trending Topics Community Trending Topics Discover the hot and trending topics in the friend circle.
Personal RankingPersonal RankingRanking the web posts according to the possibility ofthe possibility of the user sharing the information.
Social RankingSocial RankingRanking the web gposts according to after‐share effect.
Mobile Social SystemMobile Social SystemComeOnComeOnComeOnComeOnA mobile social network for social activity recommendationUsers can issue join comment and forward social activitiesUsers can issue, join, comment and forward social activities.Incorporate heterogeneous contexts for activity and target
users recommendations.
OutlineOutline
• Research works overview• Behavioral Analysis and Prediction on SocialBehavioral Analysis and Prediction on Social Networks
Three elements in social networkThree elements in social network
UsersUsersRelations
InformationInformation
Data AspectsData Aspects
User Information Relations
User User profiling Behavioral analysis Structure analysisUserPreferencePopularityActive degree
GenerateShareComment
links addingLinks deletingCommunity join
etc etc etc
Information Info profilingSemantics
Diffusion dynamicsLocal flow path
Topic distributionsHotnessetc
pGlobal flow pathetc
Relations Relation profilingTypeStrengthgInfluence etc
Predictive Modeling for Social Interactional DataPredictive Modeling for Social Interactional Data
1 MUsers 1 kFeatures
1 MUsers
Posts
Posts
1
eatures
MUsers
= ×XT V UT
P P Fe
k
N N
Observed social interaction matrix
Latent space for one dimension
Latent space for another dimension
Suffering from sparsity problemSuffering from sparsity problem
• Renren– renren.com
• Tencent Weibo– t.qq.com
• Facebook style in China– 939,363 users and
• Twitter style in China– 163,661 users and
5,829,368 posts
• Density: 0.59%1,566,609 posts
• Density: 0.09%y y
Fortunately, we have priors on the interactional dimensionsFortunately, we have priors on the interactional dimensionsUserUser
UserUserUserUser
User clusterUser cluster
User User ProfilesProfilesUser User
ProfilesProfilesUser clusterUser cluster
User clusterUser cluster
How to select the How to select the fil ?fil ?User
clusterUser cluster
profiles?profiles?
info clusterinfo
cluster Relat. clusterRelat. cluster
Interactions Interactions among among ll
Info Info ProfilesProfilesInfo Info
ProfilesProfilesinfo
clusterinfo
clusterinfo
clusterinfo
cluster Relation Relation profilesprofilesRelation Relation profilesprofiles
Relat. clusterRelat. cluster
Relat. clusterRelat. cluster
clustersclusters
info clusterinfo
cluster Relat. clusterRelat. clusterBasic Hypothesis: Basic Hypothesis: ypyp
A cluster of one dimension has similar interaction patterns with a A cluster of one dimension has similar interaction patterns with a cluster of another dimension.cluster of another dimension.
Hybrid Factor Model for Social Interactional DataHybrid Factor Model for Social Interactional Data
Under the constraint ofUnder the constraint of
V priorV prior U priorU priorV prior V prior regularizerregularizer
U prior U prior regularizerregularizer+
Share Behavior PredictionShare Behavior PredictionWho will Share What?Who will Share What?
CIKM’12 (full paper)
Information AdoptionInformation Adoption MechanismMechanismInformation Adoption Information Adoption MechanismMechanism
• In Twitter, a user receives a tweet.
Click here!
Whether to Adopt the ItemWhether to Adopt the ItemWhether to Adopt the ItemWhether to Adopt the Item
• Read the content and its comments to see whether the item is interestingg
C b h h d i h h h• Care about who the sender is, whether the sender is a close friend or authoritative
Preliminary StudyPreliminary StudyPreliminary StudyPreliminary Study
Accepted cases and refused cases have different distributions in the preference‐influence space.distributions in the preference influence space.
Preliminary StudyPreliminary StudyPreliminary StudyPreliminary Study
Preferences and influences are weakly correlated for most usersfor most users.
Social Contextual FrameworkSocial Contextual FrameworkSocial Contextual FrameworkSocial Contextual Framework
ModelModel
Model (cont )Model (cont.)
Model (cont )Model (cont.)
• Block coordinate descent scheme: Gradients
• Algorithm
Experimental ResultExperimental Result
• Parameter settings
kk = 50
Experimental Result (cont )Experimental Result (cont.)
Iter. = 50
Experimental Result (cont )Experimental Result (cont.)
• RMSE and ranking‐based evaluation
—21.1%21.1%
—16.8%
Experimental Result (cont )Experimental Result (cont.)
• Precision and Recall– top‐K recommendation on Renren and TencentpWeibo
Experimental Result (cont )Experimental Result (cont.)
• F1 measure on Renren and Tencent Weibo
2 % 6 8% 8% 9 %+ 12.5% + 6.8% + 17.8% + 9.4%
Experimental Result (cont )Experimental Result (cont.)
• T‐test
Experimental Result (cont )Experimental Result (cont.)
• Statistical significance
AfterAfter‐‐Share Effect PredictionShare Effect PredictionWho should Share What?Who should Share What?
SIGIR’11 (full paper), AAAI’11
Definition: Definition: Given an item (web post or product), the percentage of a user’s ( p p ), p gfriends who click it.
The DimensionsThe Dimensions
Post VariancePost Variance
User VarianceUser Variance
Problem FormulationProblem Formulation
U1 U2 U3
b
P1
Observed
Predicted
P2
P3
Given an user, rank the web posts to shareGiven a web post, rank the users to target
ModelingModeling
1 MUsers 1 kFeatures
1 MUsers
Posts
Posts
1
eatures
MUsers
= ×XT V UT
P P Fe
k
N N
Observed social influence matrix
Latent post matrix Latent user matrix
Sparsity 0.1%p yWe need priors on users and postsWe need priors on users and posts.
Predictive FactorsPredictive Factors
Percentage of active friends
Predictive FactorsPredictive Factors
Average tie strength
Predictive FactorsPredictive Factors
The introduction of post topic groups can reduceThe introduction of post topic groups can reduce the variances of influences.
ModelingModeling
Baseline objective function
We suppose the users with similar observed predictiveWe suppose the users with similar observed predictive factors have similar distribution in latent space
User similarity matrixWe constrain the latent post space by topic distributions
User similarity matrix
Post content matrix Topic matrix
ModelingModeling
Hybrid Factor NonHybrid Factor Non‐‐Negative Matrix Factorization (HFNegative Matrix Factorization (HF‐‐NMF)NMF)Hybrid Factor NonHybrid Factor Non Negative Matrix Factorization (HFNegative Matrix Factorization (HF NMF)NMF)
SolutionSolution
Block coordinate descent scheme
ExperimentsExperiments
Dataset Statistics Comparative MethodsLogistic Regression (LR)Cox Proportional Hazards RegressionCox Proportional Hazards Regression
(CoxPH)User Averaging Influence (AvgU)Post Averaging Influence (AvgP)Post Averaging Influence (AvgP)Basic Non‐Negative Matrix Factorization
(bNMF)User Factors Constrained NMF
(bNMF+UF)Post Factors Constrained NMF (bNMF+PF)
Evaluation Measure
34K users and 43K web posts in total
PerformancePerformance
RMSE
Ranking Criterion
The advantages of HF‐NMF is more apparent in ranking evaluations.
Examplesa p esFor a user, ranking the posts
For a post, ranking the users
ParametersParametersTradeoff parametersTradeoff parameters
Consistent across datasets of different sizes.
Di i li f h hiddDimensionality of the hidden space
Stable after k>30.
Number of iteractions
The objective value and RMSE are basically synchronousIter. 15 basically synchronous.
A more recent thought…
What’s the intrinsic difference between socialrecommendation and traditional recommendation?
Users are explicitly Users are explicitly connected!connected!
The social graph isThe social graph isThe social graph is The social graph is helpful for behavior helpful for behavior
di idi iprediction.prediction.
TrustTrustInfluenceInfluenceInfluenceInfluence
CorrelationCorrelation
The problem: The problem: How to learn a complete and accurate social graph?How to learn a complete and accurate social graph?
SparsitySparsityp yp y
Social Recommendation Across Multiple Relational DomainsSocial Recommendation Across Multiple Relational DomainsSocial Recommendation Across Multiple Relational DomainsSocial Recommendation Across Multiple Relational Domains
CIKM’12 Full Paper
Hybrid Random Walk on Second‐Order Star‐Structured Graph
d d l kUpdate cross‐domain links
Hybrid RandomWalk (cont )Hybrid Random Walk (cont.)Update within‐domain links
Compare with RWR ModelsCompare with RWR Models
• Dataset– Tencent Weibo
• RWR Models– Update tie strengthp g– Social relationWeb post similarity– Web post similarity
– User label similarity
Compare with RWR Models (cont )Compare with RWR Models (cont.)
C ith B liCompare with Baselines
Reduce 17 8%MAEReduce 17.8% MAE
Insight for Cold Start UsersInsight for Cold‐Start Users
For new users, we need only 29.5% of their historical data (user‐post links) if we have their user labels, to reach the same user‐post link prediction performanceof 100% and without user label data.
3‐day user‐post link data+ user label data= 10‐day user‐post link data
Good for new usersto edit user labels first !
References• Meng jiang, Peng Cui, Fei Wang, Qiang Yang, Shiqiang Yang. Social
Recommendation Across Multiple Relational Domains. CIKM, 2012. (Full P )Paper)
• Meng jiang, Peng Cui, Rui Liu, Qiang Yang, Fei Wang, Shiqiang Yang. Social Contextual Recommendation. CIKM, 2012. (Full Paper)P C i F i W Sh i Li Mi d O Shi i Y Wh• Peng Cui, Fei Wang, Shaowei Liu, Mingdong Ou, Shiqiang Yang. Who Should Share What? Item‐level Social Influence Prediction for Users and Posts Ranking. SIGIR, 2011. (Full paper)
• Peng Cui Fei Wang Shiqiang Yang Item Level Social Influence Prediction• Peng Cui, Fei Wang, Shiqiang Yang. Item‐Level Social Influence Prediction with Probabilistic Hybrid Factor Matrix Factorization. AAAI, 2011. (Oral)
• Zhiyu Wang, Peng Cui, Lexing Xie, Wenwu Zhu, Shiqiang Yang. Analyzing Social Media via Event Facets. ACMMultimedia, 2012. (Grand ChallengeSocial Media via Event Facets. ACM Multimedia, 2012. (Grand Challenge Final List)
• Peng Cui, Fei Wang, Lifeng Sun, Shiqiang Yang. A Matrix‐Based Approach to Unsupervised Human Action Categorization. IEEE Transactions on Multimedia (TMM), vol. 11(1), pp.102‐110, 2012.
• Peng Cui, Zhiqiang Liu, Lifeng Sun, Shiqiang Yang. Hierarchical Visual Event Pattern Mining and Its Applications. Data Mining and Knowledge Discovery (DMKD) l 22 3 467 492 2011(DMKD), vol. 22, no. 3, pp. 467‐492, 2011.
Th k !Th k [email protected]@tsinghua.edu.cn