1. Maximizing the Spread of Influence through a Social Network David Kempe, Jon Kleinberg, Eva Tardos Cornell University KDD 2003

2. Social network and spread of influence Social network spreads INFLUENCE among its members – Opinions, ideas, information … “Word-of-mouth” effect in Viral Marketing

3. Motivating scenarios 1.Adoption of a new drug by doctors and patients How to reach many patients? 2.Adoption of a new book by profs and students How to reach many students? 3.Bloggers blogging and publishing weblogs Follow which blogger to get the most information? 4.Battle of Water Sensor Networks How to find the optimize sensor placement?

4. Problem setting Given – A limited budget B for initial advertising – Influence estimates between individuals Goal – Trigger a large cascade of influence Question – Which set of individuals should we target?

5. What do we have in this paper? Form models of influence in social networks Obtain data about particular network (inter-personal influence estimating) Devise algorithm to maximize spread of influence

6. Models of influence First mathematical models – [Schelling ‘70/’78], [Granovetter ‘78] Large body of subsequent work – [Rogers ‘95], [Valente ‘95], [Wasserman/Faust ‘94] Two basic classes of diffusion models: threshold and cascade General operational view: – A social network is a directed graph, each person (individual) is a node – Nodes start either active or inactive – An active node may trigger activation of neighboring nodes – Monotonicity assumption

7. Linear threshold model

8. Example Inactive Node Active Node Threshold Active neighbors v w 0.5 0.3 0.2 0.5 0.1 0.4 0.3 0.2 0.6 0.2 Stop! U X

9. Independent cascade model

10. Example v w 0.5 0.3 0.2 0.5 0.1 0.4 0.3 0.2 0.6 0.2 Inactive Node Active Node Newly active node Successful attempt Unsuccessful attempt Stop! U X

11. Influence maximization problem

13. Bad news

14. Good news

15. Greedy algorithm

17. Experiment setup

18. Experiment result on IC model

19. Cost-effective Outbreak Detection in Networks Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, Natalie Glance Carnegie Mellon University KDD 2007

20. Original Greedy Inefficient!!! Redundant!!!

21. Submodularity property

22. CELF algorithm 700 times faster than the original greedy!!!

23. Efficient Influence Maximization in Social Networks Wei Chen, Yajun Wang, Siyu Yang Microsoft Research, Tsinghua University KDD 2009

24. Improved greedy

25. 15-34% faster than the original greedy!!!

26. Mix with CELF Cons – CELF must consider all vertices to be added in the first round, but then we can decreased in future rounds – Improved greedy must build G’ for R times Mix – First vertex: use Improved greedy – Other vertices: use CELF