1. With each device or application that expands the bandwidth of available information, the computer ’ s understanding of us remains unchanged.

2. 2 Social Network Analyses about Web Services

3. 3 Examples Hyperlink structure between personal homepages (Adamic and Adar, 2003) Discussion relationship in BBS (Goh et al., 2006) Recommendation networks in Amazon (Leskovec et al., 2007) Interaction Patterns in Yahoo Answers (Adamic et al., 2008) Friendship and followship in Twitter (Huberman et al., 2009) Community structure in Facebook (Traud et al., 2011) And so on, and so forth…

4. 4 Friendship Networks

5. 5 Friends and neighbors on the Web (Adamic and Adar, 2003) Data: Students’ homepages at (a) Stanford and (b) MIT Result:

6. 6 Adamic and Adar (2003) (2/2) Summary of links given and received among personal homepages: Power Law

7. 7 Find Me If You Can: Improving Geographical Predictionwith Social and Spatial Proximity (Backstrom et al., 2010) Data: Fackbook Result:

8. 8 Backstrom et al. (2010) (2/2)

9. 9 Social networks that matter: Twitter under the microscope (Huberman et al., 2009) Data: Twitter Result:

10. 10 Huberman et al. (2009) (2/4)

11. 11 Reciprocal friends Huberman et al. (2009) (3/4)

12. 12 Huberman et al. (2009) (4/4) It’s friends that matter

13. 13 Friendship networks and social status (Ball and Newman, 2012) Data: Friendships among students at US high and junior high schools Result:

14. 14 Ball and Newman (2012) (2/3)

15. 15 Ball and Newman (2012) (3/3)

16. 16 Online Discussion Networks

17. 17 Structure and evolution of online social relationships: Heterogeneity in unrestricted discussions (Goh et al., 2006) Data: A University BBS Result: Schematic network snapshots of a)the BBS network b)traditional social network

18. 18 Visualizing the Signatures of Social Roles in Online Discussion Groups (Welser et al., 2007) Data: Usenet newsgroups Result: answer person vs. discussion person

19. 19 Welser et al. (2007) (2/7)

20. 20 Welser et al. (2007) (3/7)

21. 21 Welser et al. (2007) (4/7)

22. 22 Welser et al. (2007) (5/7)

23. 23 Welser et al. (2007) (6/7)

24. 24 Welser et al. (2007) (7/7)

25. 25 Community Structure and Information Flow in Usenet: Improving Analysis with a Thread Ownership Model (McGlohon and Hurst, 2007) Data: Political newsgroups of Usenet Result: Cross-posting network

26. 26 McGlohon and Hurst (2007) (2/4) Anomalies: The points far below the fitting line (with abnormally low reply rates) are tw domains. The ones above the fitting line (high reply rates) tend to be in European domains.

27. 27 McGlohon and Hurst (2007) (3/4) The most reciprocated group ( hun.politika ) had a reciprocity of up to 0.58, and the least reciprocated group tw.bbs.soc.politics, had a reciprocity of 0.057. The low-reciprocity groups generally had low traffic (fewer than 100 authors in any given year, with the exception of tw.bbs.soc.politics ). All of Taiwan-based groups in our data had very low reciprocity.

28. 28 McGlohon and Hurst (2007) (4/4) Post ownership ratio: fr.soc.politique has a ratio of 0.92 tw.bbs.soc.politics.kmt ’s was around 0.003

29. 29 Expertise Networks in Online Communities: Structure and Algorithms (Zhang et al., 2007) Data: The Java Forum, a large online help-seeking community Result:

30. 30 Zhang et al. (2007) (2/2) In the Java Forum, there are some extremely active users who answer a lot of questions while a majority of users answer only a few. (See in degree) Likewise, many users ask only a single question, but some ask a dozen or more. (See out degree)

31. 31 Knowledge Sharing and Yahoo Answers: Everyone Knows Something (Adamic et al., 2008) Data: Yahoo Answers (YA), a large and diverse question-answer forum Result:

32. 32 Adamic et al. (2008) (2/4)

33. 33 Adamic et al. (2008) (3/4)

34. 34 Adamic et al. (2008) (4/4)

35. 35 Online Recommendation Networks

36. 36 The Dynamics of Viral Marketing (Leskovec et al., 2007) Data: a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products (Amazon?) Result:

37. 37 Leskovec et al. (2007) (2/3)

38. 38 Leskovec et al. (2007) (3/3)

39. 39 Leskovec et al. (2007) (3/3)

40. 40 Social Influence and the Diffusion of User-Created Content (Bakshy et al., 2010) Data: Second Life, a massively multiplayer virtual world Result: Most assets in the data set are owned by a relatively small number of users, and very large assets of size 1,000 or greater make up less than 10% of all assets. This is the familiar long tail of content popularity.

41. 41 Bakshy et al. (2010) (2/6)

42. 42 Bakshy et al. (2010) (3/6) popularity transfers between friends transfers that result in transfers

43. 43 Bakshy et al. (2010) (4/6)

44. 44 Bakshy et al. (2010) (5/6)

45. 45 Bakshy et al. (2010) (6/6)

46. 46 Information Propagation Networks

47. 47 How to search a social network (Adamic and Adar, 2005) Data: a network of actual email contacts within HP Labs Result:

48. 48 Adamic and Adar (2005) (2/4)

49. 49 Adamic and Adar (2005) (3/4)

50. 50 Adamic and Adar (2005) (4/4) Probability of two individuals corresponding by email as a function of the distance between their cubicles Email communications within HP Labs mapped onto approximate physical location

51. 51 Organizational chart and advice network in a business unit (Krackhardt, 1996)

52. 52 (Krackhardt, 1996) (2/2)

53. 53 A Measurement-driven Analysis of Information Propagation in the Flickr Social Network (Cha et al., 2009) Data: Flickr Result:

54. 54 Cha et al. (2009) (2/3)

55. 55 Cha et al. (2009) (3/3)