Organizational Overlap onSocial Networks and itsApplicationsMitul TiwariJoint work with Cho-Jui Hsieh, Deepak Agarwal,Xiny...
Who Am I2Wednesday, May 15, 13
Outline• Motivation• Organizational Overlap Model• Problem Definition• Data Analysis• Mathematical Formulation• Experimenta...
Motivation• Social Networks : important for• Sharing and Discovery• Communication• Networking• Online Social Networks are ...
Motivation: Rich Member Profile5Wednesday, May 15, 13
Motivation: Network Is Important6Wednesday, May 15, 13
Motivation: People You May Know7Wednesday, May 15, 13
Motivation: Other Entities8Wednesday, May 15, 13
Recommender Ecosystem9Similar  ProfilesConnectionsNewsSkill  EndorsementsWednesday, May 15, 13
Motivation• Member profile contains various types of organizations• Company, Schools, Groups, ...• Can we compute edge affin...
Outline• Motivation• Organizational Overlap Model• Problem Definition• Data Analysis• Mathematical Formulation• Experimenta...
Organizational Overlap Problem• Goal: compute the probability of connection based on theorganizational time overlap• For a...
Organizational Overlap Data Analysis• Insight 1: Connection density increases with organizationaltime overlap13Wednesday, ...
Organizational Overlap Data Analysis• Insight 2: Connection density decreases with the size ofthe organizational14Wednesda...
Organizational Overlap Model15Wednesday, May 15, 13
Organizational Overlap Model16Wednesday, May 15, 13
Organizational Overlap ModelValidation• Empirical connectiondensity fits our model17Wednesday, May 15, 13
Organizational Overlap Model:Estimating !• !: organization dependentparameter• Members of smallerorganization is more like...
Outline• Motivation• Organizational Overlap Model• Problem Definition• Data Analysis• Mathematical Formulation• Experimenta...
Application: Link Prediction• Warm start: existing edges• 2 features: org. overlap timeand size• Common Neighbors (CN)• Ad...
Application: Link Prediction• Cold start: no or sparseedges• All features:• time overlap, company size,company propensity,...
Application: Community Detection• Good for candidate generation for an entity recommendationsystems, such as, companies to...
Community Detection Evaluation• Compared 3 methods• Organizational overlap based• Using social connections graph• Random: ...
Community Detection Evaluation• Virality of article updates within communities24Avg degree: 4-6 Avg degree: 12-14Wednesday...
Related Work25Wednesday, May 15, 13
Summary• Motivation• Organizational Overlap Model• Problem Definition• Data Analysis• Mathematical Formulation• Experimenta...
Acknowledgement• http://data.linkedin.com• We are hiring!• Contact: mtiwari[at]linkedin.com• Follow: @mitultiwari on Twitt...
Questions?28Wednesday, May 15, 13
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Organizational Overlap on Social Networks and its Applications

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Online social networks have become important for networking, communication, sharing, and discovery. A considerable challenge these networks face is the fact that an online social network is partially observed because two individuals might know each other, but may not have established a connection on the site. Therefore, link prediction and recommendations are important tasks for any online social network. In this paper, we address the problem of computing edge affinity between two users on a social network, based on the users belonging to organizations such as companies, schools, and online groups. We present experimental insights from social network data on organizational overlap, a novel mathematical model to compute the probability of connection between two people based on organizational overlap, and experimental validation of this model based on real social network data. We also present novel ways in which the organization overlap model can be applied to link prediction and community detection, which in itself could be useful for recommending entities to follow and generating personalized news feed.

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Organizational Overlap on Social Networks and its Applications

  1. 1. Organizational Overlap onSocial Networks and itsApplicationsMitul TiwariJoint work with Cho-Jui Hsieh, Deepak Agarwal,Xinyi (Lisa) Huang, and Sam ShahLinkedInWednesday, May 15, 13
  2. 2. Who Am I2Wednesday, May 15, 13
  3. 3. Outline• Motivation• Organizational Overlap Model• Problem Definition• Data Analysis• Mathematical Formulation• Experimental Validation• Applications• Link Prediction• Community Detection 3Wednesday, May 15, 13
  4. 4. Motivation• Social Networks : important for• Sharing and Discovery• Communication• Networking• Online Social Networks are partially observed• Link Prediction and Recommending entities are important4Wednesday, May 15, 13
  5. 5. Motivation: Rich Member Profile5Wednesday, May 15, 13
  6. 6. Motivation: Network Is Important6Wednesday, May 15, 13
  7. 7. Motivation: People You May Know7Wednesday, May 15, 13
  8. 8. Motivation: Other Entities8Wednesday, May 15, 13
  9. 9. Recommender Ecosystem9Similar  ProfilesConnectionsNewsSkill  EndorsementsWednesday, May 15, 13
  10. 10. Motivation• Member profile contains various types of organizations• Company, Schools, Groups, ...• Can we compute edge affinity based on these organizationinformation?• Useful for many applications:• Recommending members to connect (link prediction)• Recommending other entities from the same community (communitydetection)10Wednesday, May 15, 13
  11. 11. Outline• Motivation• Organizational Overlap Model• Problem Definition• Data Analysis• Mathematical Formulation• Experimental Validation• Applications• Link Prediction• Community Detection 11Wednesday, May 15, 13
  12. 12. Organizational Overlap Problem• Goal: compute the probability of connection based on theorganizational time overlap• For a pair of members (A, B) who belonged to the sameorganization and overlapped in time, we have organizationaltime overlap: T(A, B, O)• Probability that A and B are connected: P(A, B)• Assume (A, B) only one common org: P(A, B) = f(T(A, B, O), O)• A function of time overlapped in the organization O and Properties of theorganization O• In short, P(t) = f(t, O), where t=T(A,B,O)12Wednesday, May 15, 13
  13. 13. Organizational Overlap Data Analysis• Insight 1: Connection density increases with organizationaltime overlap13Wednesday, May 15, 13
  14. 14. Organizational Overlap Data Analysis• Insight 2: Connection density decreases with the size ofthe organizational14Wednesday, May 15, 13
  15. 15. Organizational Overlap Model15Wednesday, May 15, 13
  16. 16. Organizational Overlap Model16Wednesday, May 15, 13
  17. 17. Organizational Overlap ModelValidation• Empirical connectiondensity fits our model17Wednesday, May 15, 13
  18. 18. Organizational Overlap Model:Estimating !• !: organization dependentparameter• Members of smallerorganization is more likely toknow each other• Empirical and MLE estimatesfor log(!) ~ -0.8 log(|S|)18Wednesday, May 15, 13
  19. 19. Outline• Motivation• Organizational Overlap Model• Problem Definition• Data Analysis• Mathematical Formulation• Experimental Validation• Applications• Link Prediction• Community Detection19Wednesday, May 15, 13
  20. 20. Application: Link Prediction• Warm start: existing edges• 2 features: org. overlap timeand size• Common Neighbors (CN)• Adamic-Adar (AA)• Data Sets: LinkedIn, Enronemails, Wiki talk20Wednesday, May 15, 13
  21. 21. Application: Link Prediction• Cold start: no or sparseedges• All features:• time overlap, company size,company propensity, nodepropensity, ...• logistic regression model21Wednesday, May 15, 13
  22. 22. Application: Community Detection• Good for candidate generation for an entity recommendationsystems, such as, companies to follow• Graph Clustering algorithm (Graclus)• Members as nodes and an edge between any pair of nodes with overlap• Organizational overlap model for computing edge weight• Graclus: minimizes the total weight of the cuts• Evaluation using• Virality of company follow within communities• Virality of article updates22Wednesday, May 15, 13
  23. 23. Community Detection Evaluation• Compared 3 methods• Organizational overlap based• Using social connections graph• Random: partition the nodes in thesame company• Using Spread of company follow• Spread: avg # of companiesfollowed within d days of thefirst follow event• Propagation rate: norm. spread23Wednesday, May 15, 13
  24. 24. Community Detection Evaluation• Virality of article updates within communities24Avg degree: 4-6 Avg degree: 12-14Wednesday, May 15, 13
  25. 25. Related Work25Wednesday, May 15, 13
  26. 26. Summary• Motivation• Organizational Overlap Model• Problem Definition• Data Analysis• Mathematical Formulation• Experimental Validation• Applications and Evaluation• Link Prediction: cold and warm start• Community Detection26Wednesday, May 15, 13
  27. 27. Acknowledgement• http://data.linkedin.com• We are hiring!• Contact: mtiwari[at]linkedin.com• Follow: @mitultiwari on Twitter27Wednesday, May 15, 13
  28. 28. Questions?28Wednesday, May 15, 13

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