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

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

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

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

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

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

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

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

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

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

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

Demonstration and Evaluation 12/19

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

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

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

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

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

Literature Antonio Caso, Silvia Rossi, “Users Ranking in Online Social Networks to Support POI Selection in Small Groups”, University of Naples Wikipedia, “PageRank”, December 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

Thank you for your attention Questions ? 19/19