1. PageRank Identifying key users in social networks Student : Ivan Todorović, 3231/2014 Mentor : Prof. Dr Veljko Milutinović

2. Introduction Social Networks – Connecting people Sustainable revenues Full advertising potential Key Users Novel PageRank 2/19

3. What is a Key User ? Large community Affects a large number of persons Unlikely to live OSN Pay for Premium services 3/19

4. Users’ Connectivity in OSN Structural characteristics of the network Well-connected users Social Graph Centrality measures –Degree –Closeness –Betweenness 4/19

5. Users’ Communication Activity Exchange of information User interaction Activity Graph Strong/Weak connection 5/19

6. PageRank An algorithm used by Google PageRank is a link analysis algorithm Outputs a probability distribution Apply to any graph or network Personalized PageRank is used by Twitter 6/19

7. Novel PageRank Identify key users First step –Derive a weighted activity graph Second step –Determine users’ centrality scores 7/19

8. Weighted Activity Graph Users who actually communicate Graph Links Informational and Normative influence 8/19

9. Weighted Activity Graph Graph representation –Symmetric adjacency matrix Weight of an undirected activity link C ij – number of communication activities (i  j) C ji – number of communication activities (j  i) Activity Graph n – Number of users 9/19

10. Users’ Centrality Scores PageRank used by Google N – Total number of webpages O j – Number of outgoing links from page j B i – Set of web pages pointing to web page i d – dampening factor (usually set to 0.85) Novel PageRank F i – Set of users connected to i 10/19

11. Demonstration and Evaluation Facebook dataset – New Orleans –Set of users (63,731) –Set of social links (817,090) –Communication activity –832,277 wall posts –BFS Crawler 11/19

12. Demonstration and Evaluation 12/19

13. Pros and Cons Great results Complexity O(n²) Social and Activity Graph Offline contacts Direction of posts/messages Privacy risks 13/19

14. Conclusion Potential to generate sustainable revenues Easy to implement Efficient 14/19

15. Improvements Text Mining to detect influence Scan user messages Detect positive/negative user response Use it to form directed activity graph 15/19

16. Improvements 16/19 Hey, check this movie (…) Well, I don’t like comedy moves Okay, maybe we could watch this one (…) That trailer looks really good A B A B Detected negative response Influence confirmed

17. Improvements Distributed PageRank algorithm Monte Carlo approximation Perform K random walks in parallel –Walk to a random neighbor (probability 1- Ɛ ) –Terminate in current node (probability Ɛ ) After walk termination –Each node computes its PageRank value Complexity O(log n / Ɛ ) 17/19

18. Literature Antonio Caso, Silvia Rossi, “Users Ranking in Online Social Networks to Support POI Selection in Small Groups”, University of Naples Wikipedia, “PageRank”, http://en.wikipedia.org/wiki/PageRank, December 2014.http://en.wikipedia.org/wiki/PageRank Julia Heidemann, Mathias Klier,Florian Probst, “Identifying Key Users in Online Social Networks – PageRank Based Approach”, Research Paper, University of Augsburg, University of Innsbruck 18/19

19. Thank you for your attention Questions ? 19/19