1. The Dynamics of Web-based Social Networks: Membership, Relationships, and Change Jennifer Golbeck College of Information Studies, University of Maryland, College Park First Monday 2007 April 27, 2011 Heegook Jun

2. Primary Objective Major patterns of behavior in Web-based social networks 2/35

3. Outline  Introduction and Background  Web Level Trends  Network Level Trends  Conclusion  Discussion and Future Work 3

4. Social Networking  One of the biggest trends on the web  Hundreds of WBSNs(web-based social networks) −Only for social networking −with other features  Many purposes −religious to entertainment  Membership −10 ~ 100,000,000 4

5. WBSNs (Web-based Social Networks)  Users state directly who they are friends with  Collecting social network data −In the real world: difficult −In WBSNs : offer an attractive data  WBSNs Data −Huge, active, changing 5

6. Analysis of WBSNs GGrowth patterns & Dynamics AAddressed on 2 levels −W−World wide web level trend −N−Network level trend 6

7. Related Work  Social networks based on real world have been studied extensively −Observing, Surveying, studying family trees and historical doc., etc  Previous work on online communities −Survey-based approach for extracting social information  However, we study the explicitly stated social connections, rather than social interactions of users 7

8. Related Work: Kumar et al., 2006  Looks at structural patterns of WBSNs −Several features support our results  However, samples are not typical of most WBSNs −Flickr: primarily a photo sharing site −Yahoo!360: Not stand alone social network  integrated into most of the Yahoo ‘sporadic & isolated’ 8

9. Outline  Introduction and Background  Web Level Trends  Network Level Trends  Conclusion  Discussion and Future Work 9

10. Web Level Trends  Social networks can be automatically derived on the web −Online auctions, news group, discussion forum  Many online communities contain, by this loose definition  However, Websites must have explicit representation of the social network for our study 10

11. Criteria for WBSNs 1.It is accessible over the web with a web browser. 2.Users must explicitly state their relationship with other people qua stating a relationship. 3.Relationships must be visible and browsable by other users in the system. 4.The website or other web-based framework must have explicit built-in support for users making these connections to other people. 11

12. Growth in Number and Membership  “Find and list every website that met the criteria” −Maintaining a list of networks (2004 ~ 2006)  Websites has almost doubled over the 2 year  Total members grew four-fold from 115M to 490M 12 2 4

13. Growth in Number and Membership 13 Rank 1 “MySpace” – 0.1M members

14. Outline  Introduction and Background  Web Level Trends  Network Level Trends  Conclusion  Discussion and Future Work 14

15. Network Level Trends  Provides insight into the behavioral patterns of users  Look at the rates −users join and leave the network −Add and remove relationships −Patterns of relationship formation −Time and activity relate to the user’s position in the network 15

16. Network Studied – 47 Days 16 Network Mem #All MemJoin DtLast Active DtAdj ListDaily Adj Lists Buzznet32,000OOO Dogster179,000OOO Ecademy100,000OOOOO Filmtrust1,000OOOO Fotothing8,400OO Friendster32,000,000OO greatestjournal1,600,000OO HAMSTERster1,350OOOO Hipstir15,300OOO LiveJournal11,000,000OO Mobango1,100,000OOO Tribe215,000OOOO Worldshine5,500OOO

17. 1 Buzznet  User-content oriented site, with music, photos, and blogs 17

18. 2 Dogster  Social network for dogs 18

19. 3 Ecademy  Business oriented social network with 100,000 members 19

20. 4 FilmTrust  Users rate movies, write reviews, and rate the trustworthiness of their friends 20

21. 5 Fotothing  Photo blogging website combined with a social network 21

22. 6 GreatestJournal  Blogging and photo sharing site 22

23. 7 Friendster  One of the original massively popular social networks 23

24. 8 HAMSTERster  Small social network for hamsters 24

25. 9 Hipstir  General social networking site 25

26. 10 LiveJournal  Blogging website with an underlying social network 26

27. 11 Mobango  Media for m.p. sharing is its main purpose  Social network is layered on top of the site 27

28. 12 Tribe  Community-oriented social network 28

29. 13 Worldshine  Travel site: book flights&hotels, find info. about destinations 29

30. Network Level Trends I.Network + II.Network - III.Relationship + IV.Relationship - V.Relationship 0 VI.Centrality 30

31. I. Patterns of Network Growth 31 Network Mem #All MemJoin DtLast Active DtAdj ListDaily Adj Lists Buzznet32,000OOO Dogster179,000OOO Ecademy100,000OOOOO Filmtrust1,000OOOO Fotothing8,400OO Friendster32,000,000OO greatestjournal1,600,000OO HAMSTERster1,350OOOO Hipstir15,300OOO LiveJournal11,000,000OO Mobango1,100,000OOO Tribe215,000OOOO Worldshine5,500OOO

32. I. Patterns of Network Growth 32

33. I. Patterns of Network Growth  Networks range widely in their purpose and size  However, it shows that regardless of these issues  Living networks tend to grow at a linear rate  Affected up or down mostly by publicity and recruitment 33

34. I. Patterns of Network Growth  2002: Biz Edu Network  Business Networking  2006: Site redesign 34

35. I. Patterns of Network Growth  Logarithmic growth = sign of dying network 35

36. II. Profile Deletion  Rate at which members leave a network  It takes some effort to delete an account from a network  Common intuition −It is easier to just abandon the account and never use it 36

37. II. Profile Deletion  Ecademy, Fotothing −No members left −No instructions on how to delete profiles  Buzznet, Tribe −Some members leave (avg 12 members/day) −Easy to find instructions on how to delete profiles  The members who left had only a small impact on the growth rate 37

38. Network Level Trends I.Network + II.Network - III.Relationship + IV.Relationship - V.Relationship 0 VI.Centrality 38

39. III. Relationship Addition  “Social” part of social networking  Networks are becoming more densely connected as thy grow  Growth in membership < Increase in relationships  (In FotoThing: 2.71 times respectively) 39

40. IV. Relationship Removal  Easier than removing a whole profile from the network  Very straightforward mechanisms for removing friends  However, −Social implications of deleting friends can discourage users from doing so −Little is gained by deleting friends −Many people strive to get as many friends as possible  Much slower rate than relationship addition 40

41. V. Non-relationship: Friendless  Percentages of friendless users vary widely  How much of non-networking functionality each site has  Pet site: social purpose, preventing from connecting are few  Ecademy, Friendster: Traditional social networking sites  Mobango, Whorldshine: have other rich functionality 41

42. V. Non-relationship: Outsiders  Friendless + members who have some social connections  Generally, Outsiders ≈ Friendless  Small Network (FilmTrust, HAMSTERster) −Significantly larger than the number of friendless members 42 The FilmTrust social network

43. Network Level Trends I.Network + II.Network - III.Relationship + IV.Relationship - V.Relationship 0 VI.Centrality 43

44. VI. Centrality  Centrality is an important measure of influence and activity in social networks  The large central cluster contains a majority of the users  There are many measures of centrality −APL(Average Path Length) −How close nodes were to the center of the main cluster 44

45. VI. Centrality  The users who tend to be toward the center of the cluster −with the most number of friends  No significant correlation b/w centrality and join date  Last Activity correlates more strongly with centrality  Activity Duration has a highest correlation  (APL decreases = indicating a more central node) 45

46. VI. Centrality  Being active in the network means more opportunities to create social connections  Person who has more social connections = the lower APL  Users who have joined more recently  the lower APL  Members who have been active longest  the lowest APL 46

47. Conclusion  This network-level analysis provides insights into many aspects of web-based social network  Membership Growth −At linear rate  Profile Deletion −Members rarely delete  Relationship Dynamics −Addition/Deletion  Social Disconnection −Friendless/Outsider (non-social features)  Centrality −users with the most # of friends 47

48. Network Level Trends I.Network + II.Network - III.Relationship + IV.Relationship - V.Relationship 0 VI.Centrality 48

49. Conclusion  This network-level analysis provides insights into many aspects of web-based social network  Membership Growth −At linear rate  Profile Deletion −Members rarely delete  Relationship Dynamics −Addition/Deletion  Social Disconnection −Friendless/Outsider (non-social features)  Centrality −users with the most # of friends 49

50. Discussion and Future Work  Study of WBSNs and dynamics, using many networks and data collected over 2 years −Web level / network level −Results on factors that cause users to join and leave  Future Work −community behavior within the networks −Egocentric trends 50

51. Thank You! Any Questions or Comments?