1. Jure Leskovec Stanford University

3. Large on-line applications with hundreds of millions of users The Web is my “laboratory” for understanding the pulse of humanity. 3

4. 4 Model as an interaction network

6. Collect 40 million articles, posts / day Study media ecosystem at large

7. 2008 U.S. presidential election campaign

8. 8 Blogs trail mass media for 2.5h on average (but professional blogs lead!)

9. Newspapers Pro Blogs TV News Agencies Blogs Entertainment

10. 10 Blogs Mainstream media

11. Blogs Mainstream media

12. Predicting new links on Facebook Out of 20k we select 20 and get 8 right!

13. ? + + + + + – – – – – > 90% accuracy –

14. 14 Observations: Big data Models: Predictions Algorithms: Applications Actively influencing the system

15. 15 … to evolve into a “happy” network? How to change human behaviors.. Observations: Data analysis Models: Predictions Algorithms: Applications Actively influencing the system

16. 16

17.  Start with intuition [Heider ’46]  Friend of my friend is my friend  Enemy of enemy is my friend  Enemy of friend is my enemy + + + - - + + + - - - - Unbalanced Balanced Consistent with “friend of a friend” or “enemy of the enemy” intuition Inconsistent with the “friend of a friend” or “enemy of the enemy” intuition

18.  Status [Davis-Leinhardt ‘68]  A  B: B has higher status than A  A  B: B has lower status than A  Status and balance don’t agree: + – AB X - - AB X + + Balance: + Status: – Balance: + Status: – Balance: + Status: – Balance: + Status: – AB X - + Balance: – Status: – Balance: – Status: – [CHI ‘10]

19.  Social networks are organized based on status..and not on friend-of-a-friend  Predicting friends and foes  ~90% accuracy  Same model works everywhere AB X ++ Balance: + Status: – Balance: + Status: – + + ? + + + + + – – – – – – – [WWW ‘10]

20. Predicting links on We get ~50% of them right But, what is global utility? Observations: Data analysis Models: Predictions Algorithms: Applications Actively influencing the system 20 vs. How to evolve into a “happy” network? Too sparse Too “cluttered”

21.  Why are networks organized the way they are?  Build models/understanding  Make predictions  Large data  Observe/model patterns not visible at smaller scales

24.  Three axioms of Physchohistory:  Detailed data about the population  Population should be sufficiently large  The population should remain in ignorance of the results Can we do it today?

25.  combines history, sociology, and mathematical statistics to make (nearly) exact predictions of the collective actions of very large groups of people, such as the Galactic Empire.historysociology  What’s next?  psychohistorians, masters of an esoteric mathematical science that enables them to plan for the future of their galactic civilization and realize their plans by making minimal changes in the society around them

26.  Psychohistory depends on the idea that, while one cannot foresee the actions of a particular individual, the laws of statistics as applied to large groups of people could predict the general flow of future events.  Asimov used the analogy of a gas: an observer has great difficulty in predicting the motion of a single molecule in a gas, but can predict the mass action of the gas to a high level of accuracygasmass action  If we’d want to predict future we would need:  The population whose behavior is modeled should be sufficiently large  The population should remain in ignorance of the results