Behavioral Analysis and Prediction in Social Networks · (Full paper) • Peng Cui, Fei Wang, Shiqiang Yang . Item‐Level Social Influence Prediction with Probabilistic Hybrid Factor

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

[email protected]

OutlineOutline

• Research works overview• Behavioral Analysis and Prediction on SocialBehavioral Analysis and Prediction on Social Networks

OutlineOutline


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‐‐

endend

Social Social

endend

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‐‐

endend

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


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


• RMSE and ranking‐based evaluation

—21.1%21.1%

—16.8%


• Precision and Recall– top‐K recommendation on Renren and TencentpWeibo


• F1 measure on Renren and Tencent Weibo

2 % 6 8% 8% 9 %+ 12.5% + 6.8% + 17.8% + 9.4%


• T‐test


• 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


Average tie strength


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

Documents

Behavioral Analysis and Prediction in Social Networks · (Full paper) • Peng Cui, Fei Wang, Shiqiang Yang . Item‐Level Social Influence Prediction with Probabilistic Hybrid Factor