comparison of online social relations in terms of volume vs. interaction: a case study of cyworld
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Comparison of Online Social Relations in terms of Volume vs. Interaction: A Case Study of Cyworld. Hyunwoo Chun+ Haewoon Kwak + Young-Ho Eom * Yong- Yeol Ahn # Sue Moon+ Hawoong Jeong * + KAIST CS. Dept. *KAIST Physics Dept. #CCNR, Boston - PowerPoint PPT PresentationTRANSCRIPT
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Comparison of Online Social Relations in terms of Volume vs. Interaction:
A Case Study of Cyworld
Hyunwoo Chun+Haewoon Kwak+Young-Ho Eom*Yong-Yeol Ahn#
Sue Moon+Hawoong Jeong*
+ KAIST CS. Dept. *KAIST Physics Dept. #CCNR, Boston
ACM SIGCOMM Internet Measurement Conference 2008
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September 18, 2008 “Making Money from Social Ties”
“37% of adult Internet users in the U.S.use social networking sites regularly…”
2
Online social network in our life
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In online social networks,
• Social relations are useful for– Recommendation– Security– Search …
• But do “friendship” in social networks repre-sent meaningful social relations?
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Characteristics of online friendship
1. It needs no more cost once established
4
My friends do not drop me off, even if I don’t do anything (hopefully)
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Characteristics of online friendship
2. It is bi-directional
5
Haewoon is a friend of Sue
Sue is a friend of Haewoon
It is not one-sided
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Characteristics of online friendship
3. All online friends are created equal
6
Ranks of friends are not explicit
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Declared online friendship
• Does not always represent meaningful social relations
• We need other informative features that rep-resent user relations in online social networks.
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User interactions
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User interaction in OSN
1. Requires time & effort
9
Leaving a message needs time
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User interaction in OSN
2. Is directional
10
But, I’ve been only thinking about what to writefor two weeks
Your friend may not reply back
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User interaction in OSN
3. Has different strength of ties
11
3 msg
0 msg yetThere are close friends and acquaintances
10 msg
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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
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Outline
• Introduction to Cyworld• User activity analysis– Topological characteristics– Microscopic interaction pattern– Other interesting observations
• Summary
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Cyworld http://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
14http://www.cyworld.com
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Three types of analyses
• Topological characteristics– Degree distribution – Clustering coefficient– Degree correlation
• Microscopic interaction pattern• Other interesting observations
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Activity network
< From, To, When ><A, C, 20040103T1103><B, C, 20040103T1106><C, B, 20040104T1201><B, C, 20040104T0159>
16
CA
B
1
2 1
Directed &weighted network
Guestbook logs
Graphconstruction
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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
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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
18Nature Reviews Genetics 5, 101-113, 2004
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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
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#(out-edges)
#(in-edges)
#(mutual-edges)
#(friends)
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Users with degree > 200 is 1% of all users
200
0.01
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Rapid drop represents the limitation of writing capability
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The gap between #(out edges) and #(mutual edges) represent partners who do not write back
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Multi-scaling behavior implies heterogeneous relations
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Clustering coefficient
25http://en.wikipedia.org/wiki/Clustering_coefficient
Ci is the probability that neighbors of node i are connected
i i i
Ci Ci Ci
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Weighted clustering coefficient
26PNAS, 101(11):3747–3752, 2004
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Weighted clustering coefficient
27PNAS, 101(11):3747–3752, 2004
i1 w = 10w = 1
i2
485.6)
2)11()110((
)13(121
1
w
iC 4811)
2)110()101((
)13(121
2
w
iC
wi
wi CC 21
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Weighted clustering coefficient
28PNAS, 101(11):3747–3752, 2004
w = 10w = 1
4211)
2)110()110((
)13(211
1
w
iC 425.15)
2)110()1010((
)13(211
2
w
iC
wi
wi CC 21
If edges with large weights are more likely to form a triad, Ci
w becomes larger
i1 i2
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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
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Degree correlation
• Is correlation between – #(neighbors) and avg. of #(neighbors’ neighbor)
• Do hubs interact with other hubs?
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Degree correlation of social network
31
degree
avg.degree
ofneighbors
Social network
Phys. Rev. Lett. 89, 208701 (2002).
“Assortative mixing”
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Degree correlation of activity network
32
We find positive correlation
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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
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Our analysis
• Topological characteristics• Microscopic interaction pattern– Reciprocity– Disparity– Network motif
• Other interesting observations
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Reciprocity
• Quantitative measure of reciprocal interaction• #(sent msgs) vs. #(received msgs)
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Reciprocity in user activities
36
y=x
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Reciprocity in user activities
37
y=x#(sent msgs) ≈ #(received msgs)
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Reciprocity in user activities
38
y=x
#(sent msgs) >> #(received msgs)
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Reciprocity in user activities
39
y=x#(sent msgs) << #(received msgs)
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Disparity
• Do users interact evenly with all friends?
Journal of Physics A: Mathematical and General, 20:5273–5288, 1987. 40
For node i,
Y(k) is average over all nodes of degree k
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Interpretation of Y(k)
Nature 427, 839 – 843, 2004 41
Communicate evenly Have dominant partner
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Disparity in user activities
42
Users of degree < 200 have a domi-nant partner in communication
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Disparity in user activities
43
Users of degree > 1000 communicate with partners evenly
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Disparity in user activities
44
Communication pattern changes by #(partners)
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Network Motifs
• All possible interaction patterns with 3 users
• Proportions of each pattern (motif) determine the characteristic of the entire network
45Science, Vol. 298, 824-827
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Motif analysis in complex networks
Science, Vol. 303, no. 5663, pp 1538-1542, 2004 46
Transcription in bacteria
Neuron
WWW & Social network
Language
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Motif analysis in complex networks
Science, Vol. 303, no. 5663, pp 1538-1542, 2004 47
In social networks, triads are more likely to be observed
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Network motifs in user activities
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As previously predicted, triads were also common in Cyworld
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Network motifs in user activities
49
Motifs 1 and 2 are also common
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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
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Our analysis
• Topological characteristics• Microscopic interaction pattern• Other interesting observations– Inflation of #(friends)– Time interval between msg
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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?
52Journal of Computer-Mediated Communication, Volume 13 Issue 3, Pages 531 – 549
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#(friends) stimulate interaction?
53
The more friends one has (up to 200), the more active one is.Median
#(sent msgs)
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Dunbar’s number
54Behavioral and brain scineces, 16(4):681–735, 1993
The maximum number of social relations managed by modern human is 150.
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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
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Time interval between msgs
• Is there a particular temporal pattern in writ -ing a msg?
• Bursts in human dynamics– e-mail– MSN messenger
56Nature, 435:207–211, 2005Proceedings of WWW2008, 2008
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Time interval between msgs
57Nature, 435:207–211, 2005Proceedings of WWW2008, 2008
intra-session
inter-session
daily-peak
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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
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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
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BACKUP SLIDES
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12M
4M
16M
8M
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Strong points
• Complete data • Huge OSN
69
Limitations
• No contents• No user profiles
• (Potential) spam msgs
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Why didn’t we filter spam?
Q: Are all msgs 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 monthare not spammers but active users…
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http://www.xkcd.com/256/ 71
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Period 2003. 6 ~ 2005.10
# of msgs 8.4B
# of users 17M
Dataset statistics
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P(k) of Cyworld friends network
Proceedings of WWW2007, 835-844, 2007 73
Multi-scaling behavior represents heterogeneous user relations