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Organizational Overlap on Social Networks and its Applications

Organizational Overlap on Social Networks and its Applications

WWW 2013 presentations slides for our paper.
Abstract:
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.

WWW 2013 presentations slides for our paper.
Abstract:
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 on Social Networks and its Applications Mitul Tiwari Joint work with Cho-Jui Hsieh, Deepak Agarwal, Xinyi (Lisa) Huang, and Sam Shah LinkedIn Wednesday, May 15, 13
  2. 2. Who Am I 2 Wednesday, May 15, 13
  3. 3. Outline • Motivation • Organizational Overlap Model • Problem Definition • Data Analysis • Mathematical Formulation • Experimental Validation • Applications • Link Prediction • Community Detection 3 Wednesday, 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 important 4 Wednesday, May 15, 13
  5. 5. Motivation: Rich Member Profile 5 Wednesday, May 15, 13
  6. 6. Motivation: Network Is Important 6 Wednesday, May 15, 13
  7. 7. Motivation: People You May Know 7 Wednesday, May 15, 13
  8. 8. Motivation: Other Entities 8 Wednesday, May 15, 13
  9. 9. Recommender Ecosystem 9 Similar  Profiles Connections News Skill  Endorsements Wednesday, May 15, 13
  10. 10. Motivation • Member profile contains various types of organizations • Company, Schools, Groups, ... • Can we compute edge affinity based on these organization information? • Useful for many applications: • Recommending members to connect (link prediction) • Recommending other entities from the same community (community detection) 10 Wednesday, May 15, 13
  11. 11. Outline • Motivation • Organizational Overlap Model • Problem Definition • Data Analysis • Mathematical Formulation • Experimental Validation • Applications • Link Prediction • Community Detection 11 Wednesday, May 15, 13
  12. 12. Organizational Overlap Problem • Goal: compute the probability of connection based on the organizational time overlap • For a pair of members (A, B) who belonged to the same organization and overlapped in time, we have organizational time 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 the organization O • In short, P(t) = f(t, O), where t=T(A,B,O) 12 Wednesday, May 15, 13
  13. 13. Organizational Overlap Data Analysis • Insight 1: Connection density increases with organizational time overlap 13 Wednesday, May 15, 13
  14. 14. Organizational Overlap Data Analysis • Insight 2: Connection density decreases with the size of the organizational 14 Wednesday, May 15, 13
  15. 15. Organizational Overlap Model 15 Wednesday, May 15, 13
  16. 16. Organizational Overlap Model 16 Wednesday, May 15, 13
  17. 17. Organizational Overlap Model Validation • Empirical connection density fits our model 17 Wednesday, May 15, 13
  18. 18. Organizational Overlap Model: Estimating ! • !: organization dependent parameter • Members of smaller organization is more likely to know each other • Empirical and MLE estimates for log(!) ~ -0.8 log(|S|) 18 Wednesday, May 15, 13
  19. 19. Outline • Motivation • Organizational Overlap Model • Problem Definition • Data Analysis • Mathematical Formulation • Experimental Validation • Applications • Link Prediction • Community Detection 19 Wednesday, May 15, 13
  20. 20. Application: Link Prediction • Warm start: existing edges • 2 features: org. overlap time and size • Common Neighbors (CN) • Adamic-Adar (AA) • Data Sets: LinkedIn, Enron emails, Wiki talk 20 Wednesday, May 15, 13
  21. 21. Application: Link Prediction • Cold start: no or sparse edges • All features: • time overlap, company size, company propensity, node propensity, ... • logistic regression model 21 Wednesday, May 15, 13
  22. 22. Application: Community Detection • Good for candidate generation for an entity recommendation systems, 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 updates 22 Wednesday, May 15, 13
  23. 23. Community Detection Evaluation • Compared 3 methods • Organizational overlap based • Using social connections graph • Random: partition the nodes in the same company • Using Spread of company follow • Spread: avg # of companies followed within d days of the first follow event • Propagation rate: norm. spread 23 Wednesday, May 15, 13
  24. 24. Community Detection Evaluation • Virality of article updates within communities 24 Avg degree: 4-6 Avg degree: 12-14 Wednesday, May 15, 13
  25. 25. Related Work 25 Wednesday, 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 Detection 26 Wednesday, May 15, 13
  27. 27. Acknowledgement • http://data.linkedin.com • We are hiring! • Contact: mtiwari[at]linkedin.com • Follow: @mitultiwari on Twitter 27 Wednesday, May 15, 13
  28. 28. Questions? 28 Wednesday, May 15, 13

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