1. Social Networks 101 P ROF. J ASON H ARTLINE AND P ROF. N ICOLE I MMORLICA

2. The nuts and bolts C OURSE WEBSITE http://theory.eecs.northwestern.edu/courses/ socialnetworks101/ G RADING 40% exams 60% participation & homework

3. Exams Q UIZ O NE : April 24, 10% Q UIZ T WO : May 15, 10% F INAL E XAM : exam week, 20%

4. Weekly assignments R EADINGS : notes, Norris Center Bookstore B LOG POSTS : linked to from website P ROBLEMS : posted on website

5. Blogging Post links to popular media (news stories, books) relating to course content, or comment on someone else’s post. Discuss how this relates to the course content.

6. Point system Problems: 6-10 pts Blogging: 1 pt each (4 max) Games: 2-4 pts ~ 10 pts per week (15 max) … for homework and participation.

7. Games Y OUR E NVELOPE : At the start of lecture, take an envelope from the box at the entrance. DO NOT OPEN OR SHARE YOUR ENVELOPE WITH ANYONE! (not even your best best friend) T OP S ECRET

8. Your professors Nicole Immorlica Jason Hartline nickle@eecs.northwestern.eduhartline@eecs.northwestern.edu Office: Ford 3-327 Office: Ford 3-329 Office hours: Monday, 3-4 Office hours: Thursday, 3-4

9. Your TAs Matthew Burgess Jack Schlesinger Mykell Miller matthewburgess2007 @u.northwestern.edu mykellmillerjohnschlesinger2009

10. Lecture One: How connected are we?

11. Question: How did the virtual disease Corrupted Blood infect the entire population of the online role-playing game World of Warcraft in under a single day?

12. How long until we die of the plague? Experiment: 1. Introduce yourself to the person on your left and right. You now know their names. 2. A volunteer will stand up. 3. Upon cue, stand up if you know someone who is standing.

13. You’re all dead. (But you have 2 points!)

14. What is a social network? A set of relationships between entities

15. Representing Social Networks 12345678 ? ? ? ?? ? ? ? … a graph!

16. Graphs 12345678 a set V of n nodes or vertices, usually denoted {v 1, …, v n } a set E of m edges between nodes, usually denoted {e i,j } 83 edge e 8,3 node v 2

17. Edges “I know you.”

18. Paths 12837456 Definition: A path is a sequence of nodes (v 1, …, v k ) such that for any adjacent pair v i and v i+1, there’s an edge e i,i+1 between them. Path (v 1,v 2,v 8,v 3,v 7 )

19. Paths “I know someone who knows someone who knows you.”

20. Path length 12837456 Definition: The length of a path is the number of edges it contains. Path (v 1,v 2,v 8,v 3,v 7 ) has length 4.

21. Distance 12837456 Definition: The distance between nodes v i and v j is the length of the shortest path connecting them. The distance between v 1 and v 7 is 3.

22. Famous distances nodes = {actors} edges = if two actors star in same film Kevin Bacon number = distance between actor and Bacon Kevin Bacon number

23. Famous distances Kevin Bacon number of … http://oracleofbacon.org/

24. Famous distances Kevin Bacon number of … http://oracleofbacon.org/

25. Famous distances nodes = {mathematicians} edges = if 2 mathematicians co-author a paper Erdos number = distance between mathematican and Erdos Paul Erdos number

26. Famous distances Erdos number of … http://www.oakland.edu/enp/

27. Famous distances Erdos number of … http://www.oakland.edu/enp/

28. Famous distances Erdos number of … http://www.oakland.edu/enp/

29. Famous distances Erdos number of … if you do an honors project with one of us! http://www.oakland.edu/enp/

30. Diameter 12837456 Definition: The diameter of a graph is the maximum shortest-path distance between any two nodes. The diameter is 3.

31. The trace of a disease 1.Initially just one node is infected 2.All nodes with an infected friend get infected 1 Day 0Day 1Day 2

32. The trace of a disease 1 # days ≤ diameter ≤ twice # days Day 2

33. The trace of a disease Because the trace defines the distance from the initially infected person to the last infected person. 1 # days ≤ diameter

34. The trace of a disease Because there’s a path in the trace between any two people going through the initially infected person. 1 diameter ≤ twice # days

35. Six degrees of separation The diameter of a social network is typically small.

36. But in real life … Not everyone has two new friends! How can we bound diameter in real social networks??? Stay tuned next week for the conclusion of this exciting story!

37. Weeks 2-4: Networks How do networks form? rich-get-richer models, preferential attachment, small-world graphs How does information spread? Milgrom’s experiment, decentralized search, wisdom of the crowds

38. Next time game theory