Modeling Relationship Strength in Online Social Networks Rongjian Xiang 1, Jennifer Neville 1, Monica Rogati 2 1 Purdue University, 2 LinkedIn WWW Summarized and Presented by Sang-il Song, IDS Lab., Seoul National University

Copyright  2010 by CEBT Introduction – Social Network  Homophily ( 동질성 ) the tendency of individuals to associate and bond with similar others “Birds of a feather flock together” Found in many real-world and online social networks  Research Area Network Structure Analysis Link prediction – “Who will be my friend?” Community Detection Item Recommendation 2

Copyright  2010 by CEBT Introduction  Past work has focused on social networks with binary ties. e.g., friends or not  Binary indicators provide only a coarse indication of the relationship. Pairs of individuals with strong ties (e.g., close friends) are likely to exhibit greater similarity then those with weak ties (e.g., acquaintances) Treating all relationships as equal will increase the noise and degrade the performance  Pruning away spurious relationships and highlighting stronger relationship has improved the accuracy of the models. 3

Copyright  2010 by CEBT Related Works  I. Kahanda and J. Neviile. Using transactional information to predict link strength in online social networks, ICWSM09  E. Gilbert and K. Karahalios. Predicting tie strength with social media. CHI 09 Binary prediction task – Strong ties or Weak ties Supervised learning – Involved in efforts on human annotations Friendship Rating Top friend nomination 4

Copyright  2010 by CEBT Goal  A model to infer relationship strength Based on profile similarity and interaction activity Automatically distinguishing strong relationships from weak ones – Unsupervised Relationship strength is represented as continuous value – Full spectrum of relation strength, from weak to strong Scalable approach – Suitable for online application 5

Copyright  2010 by CEBT Assumptions of the Model  The higher the similarity, the stronger the tie There is many common feature between ‘ 용진 ’ and me, so we have strong relationship.  Relationship strength directly impacts the nature and frequency of online interactions between a pair users ‘ 청림 ’ is close with me if he has many chat with me in messenger.  The independence of interactions 6

Copyright  2010 by CEBT Variables of the Model 7

Copyright  2010 by CEBT Graphical model representation 8

Copyright  2010 by CEBT Model Specification 9 Weighted sum of similarity measures p 0 To be estimated z Blue represents similar two users Red represents unsimilar two users

Copyright  2010 by CEBT Model Specification 10 Weighted sum of auxiliary variables and z

Copyright  2010 by CEBT Model Specification 11

Copyright  2010 by CEBT Inference 12

Copyright  2010 by CEBT Experiment  Two dataset is prepared for experiments LinkedIn – Business-Oriented Social Network – Members can search member profiles and job postings Facebook Data 13

Copyright  2010 by CEBT LinkedIn Dataset 14

Copyright  2010 by CEBT Evaluation (in LinkedIn Dataset)  Estimating relationship strength with Job Functional area Geographical region  Measuring how well the estimated relationship strengths Identifying feature values ( same school, same company, same industry) Measuring the are under the ROC curve (AUC)  Comparing relationship strength to Recommendation links Profile view links Address book links Connection links Interaction count Profile similarity 15

Copyright  2010 by CEBT Receiver Operating Characteristic (ROC)  TPR (sensitivity) eqv. with hit rate, recall TPR = TP / P = TP / (TP + FN)  FPR eqv. with fall-out FPR = FP / N = FP / (FP + TN)  AUC (Area Under ROC Curve) 16

Copyright  2010 by CEBT The result on LinkedIn dataset 17

Copyright  2010 by CEBT Facebook dataset 18

Copyright  2010 by CEBT Evaluation (in Facebook Dataset)  Comparing the relationship strength of the model to other weighted graph Friendship graph: strong/weak relationships Top-Friend graph: strong relationships Wall graph: interactions Picture graph: interactions  Evaluating Autocorrelation improvement Classification improvement 19

Copyright  2010 by CEBT Evaluation (in Facebook Dataset) 20

Copyright  2010 by CEBT Autocorrelation improvement 21

Copyright  2010 by CEBT Classification improvement 22

Copyright  2010 by CEBT Conclusions  A latent variable model for the task of relationship strength estimation Latent variable model capture the causality of the underlying social process Hybrid approach of generative model and discriminative model – Not suffering from sparsity of interaction – The latent variable is inferred using only upper level in model – Predicting future interactions is also possible Predicting new connections  Experiments show estimated relationship strength gives higher autocorrelation and better classification performance 23

Copyright  2010 by CEBT Discussions  General model to estimate relationship strength Easy to apply specific domain knowledge – Just define similarity of two users and interaction distributions  But, Experiment is something weird No comparison to other state-of-the-art techniques – There is only comparison to raw data Similarity function is too simple – Considering the recent techniques 24

Thank you 25