PageRankIdentifying key users in social networksStudent : Ivan Todorović, 3231/2014Mentor : Prof. Dr Veljko Milutinović

Introduction

• Social Networks – Connecting people

• Sustainable revenues

• Full advertising potential

• Key Users

• Novel PageRank

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What is a Key User ?

• Large community

• Affects a large number of persons

• Unlikely to live OSN

• Pay for Premium services

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Users’ Connectivity in OSN

• Structural characteristics of the network

• Well-connected users

• Social Graph

• Centrality measures– Degree– Closeness– Betweenness

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Users’ Communication Activity

• Exchange of information

• User interaction

• Activity Graph

• Strong/Weak connection

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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

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Novel PageRank

• Identify key users

• First step– Derive a weighted activity graph

• Second step– Determine users’ centrality scores

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Weighted Activity Graph

• Users who actually communicate

• Graph Links

• Informational and Normative influence

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Weighted Activity Graph

• Graph representation– Symmetric adjacency matrix

• Weight of an undirected activity link

Cij – number of communication activities (i j)

Cji – number of communication activities (j i)

• Activity Graph

n – Number of users

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Users’ Centrality Scores

• PageRank used by Google

N – Total number of webpages

Oj – Number of outgoing links from page j

Bi – Set of web pages pointing to web page i

d – dampening factor (usually set to 0.85)

• Novel PageRank

• Fi – Set of users connected to i

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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

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Demonstration and Evaluation

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Pros and Cons

• Great results

• Complexity O(n²)

• Social and Activity Graph

• Offline contacts

• Direction of posts/messages

• Privacy risks

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Conclusion

• Potential to generate sustainable revenues

• Easy to implement

• Efficient

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Improvements

• Text Mining to detect influence

• Scan user messages

• Detect positive/negative user response

• Use it to form directed activity graph

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Improvements

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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”, http://en.wikipedia.org/wiki/PageRank, December 2014.

• Julia Heidemann, Mathias Klier,Florian Probst, “Identifying Key Users in Online Social Networks – PageRank Based Approach”, Research Paper, University of Augsburg, University of Innsbruck

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Thank you for your attention

Questions ?

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