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Comparison of Online Social Relations in terms of Volume vs. Interaction: A Case Study of Cyworld Hyunwoo Chun+ HaewoonKwak+ Young-Ho Eom* 				Yong-YeolAhn# Sue Moon+ HawoongJeong* + KAIST CS. Dept.  *KAIST Physics Dept.  #CCNR, Boston ACM SIGCOMM Internet Measurement Conference 2008
2 Online social network in our life “37% of adult Internet users in the U.S. use social networking sites regularly…” September 18, 2008 “Making Money from Social Ties”
In online social networks,  Social relations are useful for Recommendation Security Search … But do “friendship” in social networks represent meaningful social relations? 3
Characteristics of online friendship It needs no more cost once established 4 My friends do not drop me off,  even if I don’t do anything (hopefully)
Characteristics of online friendship It is bi-directional 5 Haewoon is a friend of Sue It is not one-sided Sue is a friend of Haewoon
Characteristics of online friendship All online friends are created equal 6 Ranks of friends are not explicit
Declared online friendship Does not always represent meaningful social relations We need other informative features that represent user relations in online social networks. 7
8 User interactions
User interaction in OSN Requires time & effort 9 Leaving a message needs time
User interaction in OSN Is directional 10 Your friend may not reply back But, I’ve been only thinking about what to write for two weeks
User interaction in OSN Has different strength of ties 11 3 msg 10 msg There are close friends and acquaintances 0 msg yet
Our goal User interactions (direction and volume of messages) reveal meaningful social relations -> We compare declared friendship relations with actual user interactions -> We analyze user interaction patterns 12
Outline Introduction to Cyworld User activity analysis Topological characteristics Microscopic interaction pattern Other interesting observations Summary 13
Cyworldhttp://www.cyworld.com Most popular OSN in Korea (22M users) Guestbook is the most popular feature Each guestbook message has 3 attributes < From,  To,  When > We analyze 8 billion guestbook msgs of 2.5yrs 14 http://www.cyworld.com
Three types of analyses Topological characteristics Degree distribution  Clustering coefficient Degree correlation Microscopic interaction pattern Other interesting observations 15
Activity network  < From, To, When > <A, C, 20040103T1103> <B, C, 20040103T1106> <C, B, 20040104T1201> <B, C, 20040104T0159> 16 Guestbook logs  1 C A 2 Graph construction 1 B Directed & weighted network
Definition of Degree distribution 17 Degree of a node, k #(connections) it has to other nodes Degree distribution, P(k) Fraction of nodes in the network with degree k http://en.wikipedia.org/wiki/Degree_distribution
Most social networks Have power-law P(k)  A few number of high-degree nodes A large number of low-degree nodes Have common characteristics Short diameter Fault tolerant 18 Nature Reviews Genetics 5, 101-113, 2004
Degree in activity network can be defined as  #(out-edges) #(in-edges) #(mutual-edges) 19 i #(in-edges): 3 #(out-edges): 2 #(mutual-edges): 1
20 #(out-edges) #(in-edges) #(mutual-edges) #(friends)
21 0.01 200 Users with degree > 200 is 1% of all users
22 Rapid drop represents the limitation of writing capability
23 The gap between #(out edges) and #(mutual edges)  represent partners who do not write back
24 Multi-scaling behavior implies heterogeneous relations
Clustering coefficient 25 i i i Ci Ci Ci Ci is the probability that  neighbors of node i are connected http://en.wikipedia.org/wiki/Clustering_coefficient
Weighted clustering coefficient 26 PNAS, 101(11):3747–3752, 2004
Weighted clustering coefficient 27 w = 10 i1 i2 w = 1 PNAS, 101(11):3747–3752, 2004
Weighted clustering coefficient 28 w = 10 i1 i2 w = 1 If edges with large weights are more likely to form a triad,  Ciwbecomes larger PNAS, 101(11):3747–3752, 2004
Weighted clustering coefficient 29 In activity network Cw=0.0965 < C=0.1665 Edges with large weights are less likely to form a triad i1 i2
Degree correlation Is correlation between  #(neighbors) and avg. of #(neighbors’ neighbor) Do hubs interact with other hubs?  30
Degree correlation of social network 31 Social network avg. degree of neighbors “Assortative mixing” degree Phys. Rev. Lett. 89, 208701 (2002).
Degree correlation of activity network 32 We find positive correlation
From the topological structure  We find There are heterogeneous user relations Edges with large weight are less likely to be a triad Assortative mixing pattern appears 33
Our analysis Topological characteristics Microscopic interaction pattern Reciprocity Disparity Network motif Other interesting observations 34
Reciprocity Quantitative measure of reciprocal interaction #(sent msgs) vs. #(received msgs) 35
Reciprocity in user activities 36 y=x
Reciprocity in user activities 37 #(sent msgs) ≈ #(received msgs) y=x
Reciprocity in user activities 38 y=x #(sent msgs) >> #(received msgs)
Reciprocity in user activities 39 #(sent msgs) << #(received msgs) y=x
Disparity Do users interact evenly with all friends? 40 For node i, Y(k) is average over all nodes of degree k Journal of Physics A: Mathematical and General, 20:5273–5288, 1987.
Interpretation of Y(k) 41 Communicate evenly Have dominant partner  Nature 427, 839 – 843, 2004
Disparity in user activities  42 Users of degree < 200 have a dominant partner in communication
Disparity in user activities  43 Users of degree > 1000  communicate with partners evenly
Disparity in user activities  44 Communication pattern changes by #(partners)
Network Motifs All possible interaction patterns with 3 users Proportions of each pattern (motif) determine the characteristic of the entire network 45 Science, Vol. 298, 824-827
Motif analysis in complex networks 46 Transcription  in bacteria Neuron WWW & Social network Language Science, Vol. 303, no. 5663, pp 1538-1542, 2004
Motif analysis in complex networks 47 In social networks,  triads are more likely to be observed Science, Vol. 303, no. 5663, pp 1538-1542, 2004
Network motifs in user activities 48 As previously predicted, triads were also common in Cyworld
Network motifs in user activities 49 Motifs 1 and 2 are also common
From microscopic interaction pattern We find User interactions are highly reciprocal Users with <200 friends have a dominant partner, while users with >1000 friends communicate evenly Triads are often observed 50
Our analysis Topological characteristics Microscopic interaction pattern Other interesting observations Inflation of #(friends) Time interval between msg 51
Inflation of #(friends) in OSN Some social scientists mention the possibility of wrong interpretation of #(friends) In Facebook,  46% of survey respondents have neutral feelings, or even feel disconnected Do online friends encourage activities? 52 Journal of Computer-Mediated Communication, Volume 13 Issue 3, Pages 531 – 549
#(friends) stimulate interaction? 53 The more friends one has (up to 200),  the more active one is. Median #(sent msgs)
Dunbar’s number 54 The maximum number of social relations managed by modern human is 150.  Behavioral and brain scineces, 16(4):681–735, 1993
Cyworld 200 vs. Dunbar’s 150 Has human networking capacity really grown? Yes, technology helps users to manage relations No, it is only an inflated number 55
Time interval between msgs Is there a particular temporal pattern in writing a msg? Bursts in human dynamics e-mail MSN messenger 56 Nature, 435:207–211, 2005 Proceedings of WWW2008, 2008
Time interval between msgs 57 inter-session intra-session daily-peak Nature, 435:207–211, 2005 Proceedings of WWW2008, 2008
Summary The structure of activity network There are heterogeneous social relations Edges with larger weights are less likely to form a triad Assortative mixing emerges 58
Summary Microscopic analysis of user interaction Interaction is highly reciprocal Communication pattern is changed by #(partners) Triads are likely to be observed Other observations More friends, more activities (up to 200 friends) Daily-peak pattern in writing msgs 59
60
Backup slides 61
62
63
16M 12M 8M 4M 64
65
66
67
68
Strong points Complete data  Huge OSN 69 ,[object Object]
No user profiles
(Potential) spam msgsLimitations
Why didn’t we filter spam? Q: Are allmsgs by automatic script spam? A: No. Some users say hello to friends by script. 70 We confirmed that   some users writing 100,000 msgs in a month are  not spammers but active users…
http://www.xkcd.com/256/  71

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Comparison of Online Social Relations in terms of Volume vs. Interaction: A Case Study of Cyworld

  • 1. Comparison of Online Social Relations in terms of Volume vs. Interaction: A Case Study of Cyworld Hyunwoo Chun+ HaewoonKwak+ Young-Ho Eom* Yong-YeolAhn# Sue Moon+ HawoongJeong* + KAIST CS. Dept. *KAIST Physics Dept. #CCNR, Boston ACM SIGCOMM Internet Measurement Conference 2008
  • 2. 2 Online social network in our life “37% of adult Internet users in the U.S. use social networking sites regularly…” September 18, 2008 “Making Money from Social Ties”
  • 3. In online social networks, Social relations are useful for Recommendation Security Search … But do “friendship” in social networks represent meaningful social relations? 3
  • 4. Characteristics of online friendship It needs no more cost once established 4 My friends do not drop me off, even if I don’t do anything (hopefully)
  • 5. Characteristics of online friendship It is bi-directional 5 Haewoon is a friend of Sue It is not one-sided Sue is a friend of Haewoon
  • 6. Characteristics of online friendship All online friends are created equal 6 Ranks of friends are not explicit
  • 7. Declared online friendship Does not always represent meaningful social relations We need other informative features that represent user relations in online social networks. 7
  • 9. User interaction in OSN Requires time & effort 9 Leaving a message needs time
  • 10. User interaction in OSN Is directional 10 Your friend may not reply back But, I’ve been only thinking about what to write for two weeks
  • 11. User interaction in OSN Has different strength of ties 11 3 msg 10 msg There are close friends and acquaintances 0 msg yet
  • 12. Our goal User interactions (direction and volume of messages) reveal meaningful social relations -> We compare declared friendship relations with actual user interactions -> We analyze user interaction patterns 12
  • 13. Outline Introduction to Cyworld User activity analysis Topological characteristics Microscopic interaction pattern Other interesting observations Summary 13
  • 14. Cyworldhttp://www.cyworld.com Most popular OSN in Korea (22M users) Guestbook is the most popular feature Each guestbook message has 3 attributes < From, To, When > We analyze 8 billion guestbook msgs of 2.5yrs 14 http://www.cyworld.com
  • 15. Three types of analyses Topological characteristics Degree distribution Clustering coefficient Degree correlation Microscopic interaction pattern Other interesting observations 15
  • 16. Activity network < From, To, When > <A, C, 20040103T1103> <B, C, 20040103T1106> <C, B, 20040104T1201> <B, C, 20040104T0159> 16 Guestbook logs 1 C A 2 Graph construction 1 B Directed & weighted network
  • 17. Definition of Degree distribution 17 Degree of a node, k #(connections) it has to other nodes Degree distribution, P(k) Fraction of nodes in the network with degree k http://en.wikipedia.org/wiki/Degree_distribution
  • 18. Most social networks Have power-law P(k) A few number of high-degree nodes A large number of low-degree nodes Have common characteristics Short diameter Fault tolerant 18 Nature Reviews Genetics 5, 101-113, 2004
  • 19. Degree in activity network can be defined as #(out-edges) #(in-edges) #(mutual-edges) 19 i #(in-edges): 3 #(out-edges): 2 #(mutual-edges): 1
  • 20. 20 #(out-edges) #(in-edges) #(mutual-edges) #(friends)
  • 21. 21 0.01 200 Users with degree > 200 is 1% of all users
  • 22. 22 Rapid drop represents the limitation of writing capability
  • 23. 23 The gap between #(out edges) and #(mutual edges) represent partners who do not write back
  • 24. 24 Multi-scaling behavior implies heterogeneous relations
  • 25. Clustering coefficient 25 i i i Ci Ci Ci Ci is the probability that neighbors of node i are connected http://en.wikipedia.org/wiki/Clustering_coefficient
  • 26. Weighted clustering coefficient 26 PNAS, 101(11):3747–3752, 2004
  • 27. Weighted clustering coefficient 27 w = 10 i1 i2 w = 1 PNAS, 101(11):3747–3752, 2004
  • 28. Weighted clustering coefficient 28 w = 10 i1 i2 w = 1 If edges with large weights are more likely to form a triad, Ciwbecomes larger PNAS, 101(11):3747–3752, 2004
  • 29. Weighted clustering coefficient 29 In activity network Cw=0.0965 < C=0.1665 Edges with large weights are less likely to form a triad i1 i2
  • 30. Degree correlation Is correlation between #(neighbors) and avg. of #(neighbors’ neighbor) Do hubs interact with other hubs? 30
  • 31. Degree correlation of social network 31 Social network avg. degree of neighbors “Assortative mixing” degree Phys. Rev. Lett. 89, 208701 (2002).
  • 32. Degree correlation of activity network 32 We find positive correlation
  • 33. From the topological structure We find There are heterogeneous user relations Edges with large weight are less likely to be a triad Assortative mixing pattern appears 33
  • 34. Our analysis Topological characteristics Microscopic interaction pattern Reciprocity Disparity Network motif Other interesting observations 34
  • 35. Reciprocity Quantitative measure of reciprocal interaction #(sent msgs) vs. #(received msgs) 35
  • 36. Reciprocity in user activities 36 y=x
  • 37. Reciprocity in user activities 37 #(sent msgs) ≈ #(received msgs) y=x
  • 38. Reciprocity in user activities 38 y=x #(sent msgs) >> #(received msgs)
  • 39. Reciprocity in user activities 39 #(sent msgs) << #(received msgs) y=x
  • 40. Disparity Do users interact evenly with all friends? 40 For node i, Y(k) is average over all nodes of degree k Journal of Physics A: Mathematical and General, 20:5273–5288, 1987.
  • 41. Interpretation of Y(k) 41 Communicate evenly Have dominant partner Nature 427, 839 – 843, 2004
  • 42. Disparity in user activities 42 Users of degree < 200 have a dominant partner in communication
  • 43. Disparity in user activities 43 Users of degree > 1000 communicate with partners evenly
  • 44. Disparity in user activities 44 Communication pattern changes by #(partners)
  • 45. Network Motifs All possible interaction patterns with 3 users Proportions of each pattern (motif) determine the characteristic of the entire network 45 Science, Vol. 298, 824-827
  • 46. Motif analysis in complex networks 46 Transcription in bacteria Neuron WWW & Social network Language Science, Vol. 303, no. 5663, pp 1538-1542, 2004
  • 47. Motif analysis in complex networks 47 In social networks, triads are more likely to be observed Science, Vol. 303, no. 5663, pp 1538-1542, 2004
  • 48. Network motifs in user activities 48 As previously predicted, triads were also common in Cyworld
  • 49. Network motifs in user activities 49 Motifs 1 and 2 are also common
  • 50. From microscopic interaction pattern We find User interactions are highly reciprocal Users with <200 friends have a dominant partner, while users with >1000 friends communicate evenly Triads are often observed 50
  • 51. Our analysis Topological characteristics Microscopic interaction pattern Other interesting observations Inflation of #(friends) Time interval between msg 51
  • 52. Inflation of #(friends) in OSN Some social scientists mention the possibility of wrong interpretation of #(friends) In Facebook, 46% of survey respondents have neutral feelings, or even feel disconnected Do online friends encourage activities? 52 Journal of Computer-Mediated Communication, Volume 13 Issue 3, Pages 531 – 549
  • 53. #(friends) stimulate interaction? 53 The more friends one has (up to 200), the more active one is. Median #(sent msgs)
  • 54. Dunbar’s number 54 The maximum number of social relations managed by modern human is 150. Behavioral and brain scineces, 16(4):681–735, 1993
  • 55. Cyworld 200 vs. Dunbar’s 150 Has human networking capacity really grown? Yes, technology helps users to manage relations No, it is only an inflated number 55
  • 56. Time interval between msgs Is there a particular temporal pattern in writing a msg? Bursts in human dynamics e-mail MSN messenger 56 Nature, 435:207–211, 2005 Proceedings of WWW2008, 2008
  • 57. Time interval between msgs 57 inter-session intra-session daily-peak Nature, 435:207–211, 2005 Proceedings of WWW2008, 2008
  • 58. Summary The structure of activity network There are heterogeneous social relations Edges with larger weights are less likely to form a triad Assortative mixing emerges 58
  • 59. Summary Microscopic analysis of user interaction Interaction is highly reciprocal Communication pattern is changed by #(partners) Triads are likely to be observed Other observations More friends, more activities (up to 200 friends) Daily-peak pattern in writing msgs 59
  • 60. 60
  • 62. 62
  • 63. 63
  • 64. 16M 12M 8M 4M 64
  • 65. 65
  • 66. 66
  • 67. 67
  • 68. 68
  • 69.
  • 72. Why didn’t we filter spam? Q: Are allmsgs by automatic script spam? A: No. Some users say hello to friends by script. 70 We confirmed that some users writing 100,000 msgs in a month are not spammers but active users…
  • 75. P(k) of Cyworld friends network 73 Multi-scaling behavior represents heterogeneous user relations Proceedingsof WWW2007, 835-844, 2007