Analyzing Virtual Worlds
Next Step in the Evolution of Social Science Research
Dmitri Williams, USC
Noshir Contractor, Northwestern University
Jaideep Srivastava, University of Minnesota
Marshall Scott Poole, University of Illinois at
Urbana-Champaign
Overview
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Brief Intros of PIs
Origins & EQ2
Goals & Research Agenda
Data and methods
Database structure/CS & Informatics Challenges
Early findings: Census, gender, role play, proximity,
network signatures
Case study with full metrics: Economics
Upcoming/in progress: Problematic uses, social
capital, team performance, group metrics
The PIs
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Noshir Contractor, Northwestern
Marshall Scott Poole, University of Illinois
at Urbana-Champaign
Jaideep Srivastava, Univ. of Minnesotta
Dmitri Williams, University of Southern
California
Multidimensional Networks in Virtual Worlds
Multiple Types of Nodes and Multiple Types of Relationships
SONIC
Advancing the Science of
Networks in Communities

Any churn, customer service or big-picture
material from Nosh, plus referencing the
larger issues and the Bainbridge session to
follow.
Support

U.S. National Science Foundation
◦ Small Grants for Emerging Research (SGER)
◦ Information and Intelligent Systems (IIS)

Army Research Institute
SONY Online Entertainment (SOE)
 National Center for Supercomputing Applications,
UIUC
 Annenberg School for Communication, USC

Origins
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Prior work: experimental and survey
Begging for server-side data
EQ2: Some basics:
◦ About 175,000+ players
◦ Dozens of servers worldwide
◦ Successor to Ever Quest
http://www.youtube.com/watch?v=G3VamwcAlzs
The generalizability issue

Is one title like the next?
=
?
Elements of the game
Basics
–
–
–
–
–
–
–
–
Fantasy based
Create character
Never ending quest for advancement and exploration
Underlying Storyline: Conflict between good and evil
factions
Band with other characters
Quests to kill monsters
Craftwork
Socializing
Why study these things?
MMOGs are of interest in their own right
◦ Psychological, social, economic impacts
◦ Number of users now around 45 million
MMOGs may be a mirror of the real world
◦
◦
◦
◦
◦
◦
Networks
Economics
Group Processes
Conversation
Conflict
Learning
Research framework

When does the virtual map on to the real?
Group size
Traditional controls
and independent
variables
Contextual and social
architecture factors
Directionality
Individual
Psychological profile
World size
Online to offline
Dyads
Motivations
Persistence
Offline to online
Small groups
Demographics
Competitive vs.
Collaborative
Endogenous
Large groups
Communications
medium
Role play
Communities
Network-level variables
Sandbox vs. linear
Societies
Representation
Interaction affordances
Data & Methods
Previous work…
◦ Experiments
◦ Ethnographies
◦ Surveys in separate sites
VWE Project…
◦ Proprietary in-game player data
◦ Survey of players from game
Who Plays, How Much, And Why?
Replacing selfselected survey
work, adding
basic use
patterns
 It ain’t a bunch
of kids:
 Average age is
31.16 (US
population
median is 35)
 More players in
their 30s than in
their 20s.

How much do they play?


Mean is 25.86
hours/week
Compares to
US mean of
31.5 for TV
(Hu et al, 2001)
• From prior experimental work, MMO play eats into entertainment TV
and going out, not news
• So much for kids being the ones with the free time.
Some oddball findings
EQ2 players have lower Body Mass Index than US
Population, i.e. they are physically healthier
 EQ2 players have higher levels of depression, lower
levels of anxiety than US population
 Healthiest group vs. US Pop is older women
 Women are:
- More intense players in terms of time and
commitment
- Older (33 vs. 30)
- More likely to be disabled
- Disproportionately likely to be bisexual (14% vs.
2.8% for US) or physically impaired (14.4% vs. 7.3%
for US)

Gender-based tests
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
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Gender role theory: Boys and girls are
socialized early on, and thus have clear role
expectations for their behaviors and
identities
Hypotheses based on social categories and
gendered expectations
Men indeed were more of the players
(80/20%), and played to compete vs.
women playing to socialize. However . . .
Who’s Hardcore?
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

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Men play more other games, but it was the
women who were the most dedicated and
intense satisfied EQ2 players
Women: 29.32 hours/week
Men: 25.03 hours/week
Likelihood of quitting: “no plans to quit”:
women 48.66%, men 35.08%
The self-reported play issue
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Actual play was:
Women: 29.32 hours/week
Men: 25.03 hours/week
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Self-reported play was:
Women: 26.03 (3 hours less than actual)
Men: 24.10 (1 hour less than actual)

Implications for prior research!
Playing with a partner: Apparently not
good for the gander!
Role Players: asic theory and hypotheses
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Theory base: Goffman, Meyrowitz, Turkle
How common is it, why do they do it, and who are they?
It’s not that common, despite the name of the game.
Only 5% were dedicated role players
Role players had significantly worse psychological profiles,
e.g. depression, loneliness, addictions, learning disabilities
One possible reason for play: to be accepted or to
experiment when it’s not possible to do so offline
Evidence comes from the profile
Marginalized populations
role play more
Economics: a first test of mapping
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Do players behave in virtual worlds as we
expect them to in the actual world?
Economics is an obvious dimension to test
In the real world, perfect aggregate data are
hard to get
Results
GDP and price levels are robust but comparatively unstable
GDP and Prices on Antonia Bayle
5,000,000
160
4,500,000
140
120
3,500,000
3,000,000
100
2,500,000
80
2,000,000
60
1,500,000
40
1,000,000
20
500,000
0
0
January
February
Nominal GDP
March
April
Price Level (January = 100)
May
Prices (January = 100)
4,000,000
GDP (Gold)

Results
The instability is explicable through the Quantity Theory of Money: a rapid
influx of money, combined with a drop in population . . .
Change in Money Supply and Population on Antonia Bayle
2500
4000
3000
2000
2000
1000
1500
0
-1000
1000
-2000
-3000
500
-4000
0
-5000
February
March
Change in Money Supply (000 Gold)
April
May
Change in Active Accounts
Change in Accounts
Change in Money (000 Gold)

Results
. . . dramatically boosted prices: More evidence that this behaves
like a real economy.
Price Level on Antonia Bayle
Percent Change in Price Level
50
40
30
20
10
0
February
March
April
-10
-20
Price Level
May
June
Results
A new server’s GDP and price level rapidly converge to those of an
existing server (replicability). Lessig and the mapping idea.
GDP and Prices, New and Mature Servers
5,000
4,500
4,000
160
140
120
3,500
3,000
2,500
2,000
1,500
100
80
60
40
1,000
500
0
20
0
January
February
March
April
May
Real GDP Antonia Bayle, Base Antonia Bayle January
Real GDP Nagafen, Base Antonia Bayle January
Price Level Antonia Bayle, Base Antonia Bayle January
Price Level Nagafen, Base Antonia Bayle January
Price Level
GDP (000)

Trust in Virtual Worlds
Trust in Virtual Worlds

Trust has become problematic in the late 20th and early
21st centuries
◦ Modernity and Self Identity (Giddens): Institutions that once
provided foundation for trust are changing rapidly and
creating a sense of discontinuity that creates a sense of
insecurity
◦ Bowling Alone (Putnam and Oldenberg): Sense of civil
society and community that provided basis for trust has
eroded
◦ No Sense of Place (Meyrowitz): Media connect us to others
not part of our communities—can we trust them?
Trust in Virtual Worlds

Trust and feeling part of a community is very important in
the early 21st century

Online worlds offer potential for community

Community depends on trustworthiness of online worlds

Do participants have a sense of trust in EQ2?
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Does trust work the same way in EQ2 as in RL?
Trust in Virtual Worlds
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Sources of Trust
1. Personality: Generalized Trust (Rotter)
2. Institutions: Within Online World (e.g. guilds in EQ2)
3. Communication:
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Amount
Self-disclosure
Private versus public messages
Modality
Trust in Virtual Worlds
H1: Levels of generalized trust will be positively
related to online trust
H2a: Players trust their guild members more than
others in the game
H2b: MMO players trust others in the game more
than others online
Trust in Virtual Worlds
H3a: Amount of small talk is positively related to trust of
guildmates and others in the MMO
H3b: Self-disclosure is positively related to trust of guildmates
and others in the MMO
H3c: The higher the proportion of all messages that a player
publicly sends to a guild, the more this player trusts guild
members.
H3d: People who use voice chat in the game more frequently are
more likely to trust others a) in their guild and b) in the game
the game and in their guild compared to people who use
voice chat less frequently.
Trust in Virtual Worlds
Measures:
Survey:
 Generalized Trust: “Generally speaking, would you say that most people in
everyday life can be trusted or that you can’t be too careful in dealing with
people?” [Five Point Likert Scale]
 Trust in Others Online: Same question rephrased to refer to others
online
 Trust in Others in EQ2: Same question rephrased to refer to EQ2 players
 Trust in Others in My Guild: Same question rephrased to refer to
members of guild
Trust in Virtual Worlds
Measures:
Survey:
 Self-disclosure: index of subjects’ responses to questions about a variety of
relatively sensitive topics they might have discussed while playing. These
covered how much they talked about dating or married life, parenting, sex,
religion, and politics [Five Point Likert Scale]
 Small Talk: “When you are online talking to other players, how much ‘small
talk’ (e.g. talk about the weather, short conversations about how you are
doing, etc.) goes on?” [Scale: “None at all”, “Very little”, “Some”, “A lot”, “I am
not in a guild”, and “Don’t Know”].
 Voice Chat Use: “How often do you use a voice system (e.g. TeamSpeak,
Ventrilo) to talk to other players?” [Scale: “Never”, “Rarely, less than once a
month”, “Once a week”, “About once a day, but not always”, and “Always”]
Trust in Virtual Worlds
Measures
Behavioral
Public and Private Messages: The number of private (to
individual players) and public (to guild) messages each
player sent was derived from Sony’s databases. The
proportion of all messages that players publicly sent to a
guild was calculated by dividing the number of public guild
messages by the sum of the number of private messages
and public guild messages. Higher values mean that a player
sent a higher proportion of public messages.
Trust in Virtual Worlds
Measures
Control Variables
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Generalized Trust
Gender
Amount of time used email in typical week (self-report)
Amount of time used internet in typical week (self-report)
Amount of time per week play EQ2 (self-report)
Trust in Virtual Worlds
Findings:
H1: Levels of generalized trust will be positively related to online trust
Supported: Correlations of generalized trust with other forms of trust
(online, EQ2, guild) are significant
H2a: Players trust their guild members more than others in the game
Supported:Trust of guild members is significantly greater than trust of
others in the EQ2
H2b: MMO players trust others in the game more than others online
Supported:Trust of others in EQ2 is significantly higher than trust of others
online,
Trust in Virtual Worlds
H3a: Amount of small talk is positively related to trust of
guildmates and others in the MMO
Supported: Small talk significantly predicted trust of
guildmates (medium effect size)and others in EQ2 (small
effect size); coefficients in the predicted direction.
H3b: Self-disclosure is positively related to trust of
guildmates and others in the MMO
Supported: Self-disclosure significantly predicted trust of
guildmates (small effect size) and others in EQ2 (small
effect size); coefficients in predicted direction.
Trust in Virtual Worlds
H3c: The higher the proportion of all messages that a player
publicly sends to a guild, the more this player trusts guild
members.
Supported: Players who sent a greater proportion of public
guild messages reported higher levels of trust in guild
members as well as other players.
H3d: People who use voice chat in the game more frequently
are more likely to trust others a) in their guild and b) in
the game the game and in their guild compared to people
who use voice chat less frequently.
Not Supported
Trust in Virtual Worlds
Conclusions:
◦ Trust in online world exhibits similar patterns of result
to what would be expected in RL
◦ Institutions like guilds serve an important function in
MMOs in that they provide a basis for trust
◦ MMOs engender trust and may be a basis for
community
◦ Communication has a role in generating trust in online
worlds
Expertise in Virtual Worlds
Expertise in Online Worlds

Important factor influencing effectiveness and
accomplishment in the MMO

In MMOs there are precise metrics defining
expertise and changes in expertise.

What can expertise in the MMO tell us about
expertise in RL?
Expertise in Online Worlds
Expertise = f(Motivation, Effort, Time,
Personal Characteristics)
Expertise in Online Worlds
RQ1: What is the relationship between age, gender
and game expertise?
H1a: Players who are high on achievement motivation in
game playing will have higher levels of expertise.
H1b: Players who are high on social motivation will have
lower levels of expertise.
H1c: Players who are high on immersion motivation will
have higher levels of expertise.
Expertise in Online Worlds
H2a: Players who spend more time playing the game will have
higher levels of expertise.
H2b: Players who focus their game time on completing quests will
have higher levels of expertise.
H3a: Players who are more extroverted will have higher levels of
expertise.
H3b. Players who are more aggressive will have higher levels of
expertise in the game.
Expertise in Online Worlds
Measures:
Expertise: Sum of each character’s statistics on strength, agility,
intellect, wisdom, and stamina.
Age and gender: From the survey data; the age range was coded
from 12 to 65, with “12” indicating “under 13” and 65 indicating
“65 and above.”
Play time: Total number of seconds spent on primary character.
The value was log-transformed to reduce positive skew.
Expertise in Online Worlds
Measures:
Player motivation: The measures for player motivations were adapted from
those in Yee (2007), with one additional item on chatting, which yielded 11
items in total. A principal component analysis with varimax rotation
revealed three major player motivations, which were in agreement with
the findings by Yee (2007).
 Achievement motivation focuses on advancement in the game, understanding
game mechanics, and competition with other players.
 Social motivation focuses on socialization, building long-term meaningful
relationships, and participation in team work.
 Immersion motivation focuses on exploration in the game world, role-playing,
customization of characters, and escapism. The average score for each major
motivation was calculated and included as a predictor variable in the final
model.
Expertise in Online Worlds
Measures
Game focus: Amount of time players spend doing quests as opposed to
other game activities such as utilizing trade skills or socializing with
other players. It was calculated by dividing the total number of quests
that players completed by the log-transformed play time.
Aggression: The AQ Physical Aggression subscale. On 9 statements, survey
participants were asked to indicate the degree of their agreement on a
7-point scale ranging from “Strongly Disagree” (1) to “Strongly Agree”
(7). An illustrative statement involving physical aggression was “If
somebody hits me, I hit back.” The average score across the 9 items was
calculated as the players’ score on physical aggression.
Expertise in Online Worlds
Measures:
Extroversion: Adapted from Bendig (1962). Survey respondents
were asked to judge how much they agreed with 10 statements
on a 7-point scale ranging from “Strongly Disagree” (1) to
“Strongly Agree” (7). One example item was “I like to mix socially
with people.” The average score across the 10 items was
calculated as the players’ score on extroversion
Expertise in Online Worlds
Results:
RQ1: What is the relationship between age, gender and game expertise?
Age positively related to expertise; no relationship to gender
H1a: Players who are high on achievement motivation in game
playing will have higher levels of expertise.
Supported: Achievement motivation significantly predicted expertise;
coefficient was in expected direction (small effect size)
Expertise in Online Worlds
Results:
H1b: Players who are high on social motivation will have lower levels
of expertise.
Not Supported: Social motivation significantly predicts expertise;
coefficient is positive, not negative as expected (small effect size)
H1c: Players who are high on immersion motivation will have higher
levels of expertise
Not Supported: Immersion motivation significantly predicts expertise;
coefficient is negative, not positive as expected (small effect size)
Expertise in Online Worlds
H2a: Players who spend more time playing the game will have
higher levels of expertise.
Supported:Time spent playing significantly predicted expertise;
coefficient was in predicted direction (large effect size)
H2b: Players who focus their game time on completing quests will
have higher levels of expertise.
Supported: Focus on quests significantly predicted expertise;
coefficient was in predicted direction (large effect size)
Expertise in Online Worlds
H3a: Players who are more extroverted will have higher levels of
expertise.
Supported: Extroversion significantly predicted expertise;
coefficient in predicted direction (small effect size)
H3b. Players who are more aggressive will have higher levels of
expertise in the game.
Supported:Aggression significantly predicted expertise;
coefficient in predicted direction (small effect size)
WHY DO WE CREATE
NETWORKS IN VIRTUAL
WORLDS?
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Advancing the Science of
Networks in Communities
Monge, P. R. & Contractor, N. S. (2003). Theories of
Communication Networks. New York: Oxford University Press.
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Advancing the Science of
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Social Drivers:
Why do we create and sustain networks?
Theories of selfinterest
 Theories of social and
resource exchange
 Theories of mutual
interest and collective
action






Theories of contagion
Theories of balance
Theories of homophily
Theories of proximity
Theories of coevolution
Sources:
Contractor, N. S., Wasserman, S. & Faust, K. (2006). Testing multi-theoretical multilevel hypotheses about
organizational networks: An analytic framework and empirical example. Academy of Management
Review.
Monge, P. R. & Contractor, N. S. (2003). Theories of Communication Networks. New York: Oxford
University Press.
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Advancing the Science of
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“Structural signatures” of MTML
A
A
B
B
C
+
-
F
F
+
-
C
E
E
D
D
Theories of Self interest
Theories of Exchange
Theories of Balance
A
A
B
B
F
+
C
F
-
+
E
D
C
E
D
Theories of Collective Action Theories of Homophily
Novice
Expert
Theories of Cognition
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Advancing the Science of
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Statistical “MRI” for Structural Signatures

p*/ERGM: Exponential Random Graph
Models

Statistical “Macro-scope” to detect
structural motifs in observed networks

Move from exploratory to confirmatory
network analysis to understand multitheoretical multilevel motivations for why
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we create social networks
Advancing the Science of
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Four Types of Relations in EQ2




Partnership: Two players play together in combat activities;
Instant messaging: Two players exchange messages through
Sony universal chat system
Player trade: Players meet “face-to-face” in EQ2 and one
gives items to another;
Mail: One player sends a message and/or items to others
by in-game mail
Synchronous
Interpersonal interaction
Partnership,
Instant messaging
Transactional interaction
Player trade
Asynchronous
Mail
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Hypotheses - Network
H1: (Sparsity) Individuals are not likely to engage in
interaction randomly in a virtual world.
 H2: (Popularity) Individuals with many interactions are
more likely to engage in interaction than those have a
few interactions.
 H3: (Transitivity) Two individuals who both interact
with the third parties are more likely to engage in
interaction than those do not have common parties
between them.

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Hypotheses - Proximity
H4: (Geographical) Individuals who are proximate in geographical
distance are more likely to engage in interaction than those who
are not proximate.

H4a: (Short distance) Individuals who are in close proximity are
substantially more likely to engage in interaction than those
who are at medium or low proximity.
Probability to
engage in interaction

0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
500
1000
1500
2000
2500
3000
SONIC
1
Distance between players (Km)
10
100
1000
10000
Advancing the Science of
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Hypotheses - Proximity

H4: (Geographical) Individuals who are proximate in
geographical distance are more likely to engage in
interaction than those who are not proximate.
• H4b: (Interaction types) Individuals who are proximate
are more likely to engage in interpersonal interactions
(i.e. partnership and instant messaging) than
transactional interactions (i.e. trade or mail).
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Hypotheses – Proximity (cont.)


H5: (Temporal) Individuals who are
proximate in time zone are more likely to
engage in interaction than those who have
bigger time differences.
H6: (Synchronization) Individuals who are
proximate in time zone are more likely to
engage in synchronous interactions than
asynchronous interactions.
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Hypotheses - Homophily
H7: (Gender) Individuals of the same gender are
more likely to engage in interaction than those of
opposite genders.
 H8: (Age) Individuals who have smaller age
differences are more likely to engage in
interaction than those who have bigger
differences.
 H9: (Experience) Players who have similar years
of game experience are more likely to engage in
interaction than those who have bigger
differences.

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Data Description

3140 players from Aug 25 to Aug 31 2006,
in Antonia Bayle
◦ 2998 US, 142 CA ; 2447 male, 693 female
• Demographic information
– Gender, age, and account
age (years played Sony
games)
– Zip code, state, and country
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Descriptive Statistics

3140 players from Aug 25 to Aug 31 2006, in Antonia Bayle
◦ 2998 US, 142 CA ; 2447 male, 693 female
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
Age
5.668
25.410
31.350
32.340
37.550
71.040
AcctAge
0.003
1.014
1.803
2.451
3.275
9.400
Latitude
18.27
33.88
38.89
38.28
42.11
71.29
Longitude
-158.00
-112.00
-88.38
-94.19
-80.58
50.71
Network
Node
s
Edges
Density
Degre
e
(mean
)
Degre
e
(max)
Diamet
er
Centralizati
on
(degree)
Partner
1924
1789
0.097%
1.860
14
24
0.63%
Instant
messaging
548
517
0.34%
1.887
10
27
1.49%
Trade
2456
Mail
SONIC
2090
3812
3120
0.13%
0.14%
3.104
2.986
24
84
19
19
0.85%
3.83%
Advancing the Science of
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Black: male
Red: female
Partnership
Instant messaging
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Trade
Mail
Advancing the Science of
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Construct Physical Proximity

Zip code -> latitude/longitude, time zone
◦ U.S. ZIPList5 and Canada Geocode from
ZipInfo.com

Latitude/longitude -> geographical distance
◦ Spherical law of cosines:
Distance category
acos(sin(lat

1)×sin(lat2)+cos(lat1)×cos(lat2)×cos(long2-long1))×6371 Km
◦ short (0 < r < 50 Km), intermediate (50 < r <
800 Km) and long (r > 800 Km) distances
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Analysis

Network signatures
◦ Sparsity: number of edges
◦ Polularity: geometrically weighted degree (gwdegree)
◦ Transitivity: geometrically weighted edgewise shared partner(gwesp)

Homophily measures
◦ Gender homophily: gender matching (1 for M-M or F-F; 0 for M-F match)
◦ Age homophily: age difference (absolute value in years)
◦ Experience homophily: account age difference (absolute value in years)

Proximity measures
◦ Geographical proximity: physical distance (absolute value in kilometers)
◦ Temporal proximity: time zone difference (absolute value in hours)
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p*/Exponential Random Graph Models
Analysis network data with Interdependencies –
endogenous correlation among the relations
 ERGMs are a class of stochastic models:

P(Y  y ) 
1
exp( T g ( y ))
k ( )
◦ g(y) is a vector of network statistics such as network
structural measures and node attributes
◦ θ is a vector of coefficients.
Frank & Strauss, 1986; Pattison & Wasserman, 1999; Robins, Pattison, & Wasserman, 1999;
Wasserman & Pattison, 1996; Hunter, 2007; Robins, Snijders, Wang, Handcock, & Pattison,
2007
SONIC
Advancing the Science of
Networks in Communities
Estimation Methods

p*/ERGM model variables
◦ Non-directed network: dichotomized partner,
IM, trade, and mail relations
◦ Network structures: edges, gwdegree, and
gwesp
◦ Dyadic attributes: geographical distance and
time zone difference between players
◦ Actor attributes: gender, age, and account age

Estimation tools
◦ Statnet version 2.1, R-2.8.0
◦ Social Sciences Computing Cluster (SSCC) at
NU
SONIC
Advancing the Science of
Networks in Communities
Results 0: Baseline Model
Supported: Individuals
are not likely to
engage in interaction
Hypothese
Variables
randomly.
s
Partner
Model P0
IM
Model I0
Partially supported: High degree
Individuals are more likely to
Trade
Mail
engage
in
partner
and IM relations
Model T0 Model
but notM0
trade and mail.
H1: Sparsity
Edges
-8.72***(.28)
-7.12***(.20)
-6.02***(.03)
-5.94***(.03)
H2: Popularity
GWDegree
1.07***(.18)
1.36***(.20)
-1.12***(.06)
-1.27***(.06)
H3: Transitivity
GWESP
1.49***(.03)
1.25***(.005)
Log likelihood
-13643.56
-3137.08
-29107.26
-23514.92
0.134
0.032
0.006
Supported:
If two
Degeneracy
value 0.092
individuals have shared
partners, they are more
likely to engage in partner
and IM relations.
SONIC
Signif. codes: 0 < *** < 0.001 < ** < 0.01 < * < 0.05 < + < 0.1
Advancing the Science of
Networks in Communities
Results 1:
Homophily Model
Not supported: Individuals of
the same gender are NOT
likely to engage in interaction.
Hypotheses
Variables
Partner
Model P1
IM
Model I1
Trade
Model T1
Mail
Model M1
H1: Sparsity
Edges
-8.587***(.28)
-6.687***(.20)
-5.780***(.03)
-5.525***(.03)
H2: Popularity
GWDegree
1.253***(.18)
1.407***(.20)
-0.905***(.06)
-1.145***(.06)
H3: Transitivity
GWESP
1.454***(.04)
1.237***(.04)
H7: Gender homophily
Same Gender
-0.152***(.02)
-0.148***(.03)
-0.061***(.01)
-0.059***(.01)
H8: Age homophily
Age Difference
-0.035***(.001)
-0.026***(.002)
-0.025***(.0007)
-0.031***(.001)
H9: Experience homophily
AcctAge Difference
-0.115***(.005)
-0.063***(.01)
-0.144***(.003)
-0.086***(.003)
H4,4a,4b: Geographical proximity
Log(Distance)
H5,6: Temporal proximity
Timezone difference
Control
Female
-0.201***(.02)
-0.080* (.04)
-0.041***(.01)
0.024** (.009)
Age
0.005***(.0002)
-0.002***(.0005)
AcctAge
0.007** (.002)
0.038***(.005)
0.032***(.001)
Log likelihood
-13547.59
-3123.28
-29001.57
-23386.54
Degeneracy value
0.478
0.672
2.120
5.013
Supported: Individuals with
similar age and experience are
more likely to engage in
interaction.
Signif. codes: 0 < *** < 0.001 < ** < 0.01 < * < 0.05 < + < 0.1
SONIC
Advancing the Science of
Networks in Communities
Special behavior in MMORPG:
Females have a strong
tendency to interact with
male partners
Results 1a:
Gender Detail
Hypotheses
Variables
Partner
Model P1a
IM
Model I1a
Trade
Model T1a
Mail
Model M1a
H1: Sparsity
Edges
-8.716***(.28)
-6.837***(.21)
-5.820***(.03)
-5.478***(.03)
H2: Popularity
GWDegree
1.223***(.18)
1.381***(.21)
-0.901***(.05)
-1.154***(.06)
H3: Transitivity
GWESP
1.481***(.05)
1.172***(.04)
H7: Gender homophily
Male-male match
0.041* (.02)
-0.067+ (.03)
-0.019* (.009)
-0.114***(.01)
Female-female match
-0.369***(.06)
-0.209* (.10)
-0.115** (.04)
-0.038 (.04)
H8: Age homophily
Age Difference
-0.035***(.001)
-0.025***(.002)
-0.025***(.001)
-0.030***(.0006)
H9: Experience homophily
AcctAge Difference
-0.115***(.006)
-0.057***(.01)
-0.144***(.003)
-0.085***(.003)
H4,4a,4b: Geographical proximity
Log(Distance)
H5,6: Temporal proximity
Timezone difference
Control
Female
0.005***(.0002)
-0.002**
Age
(.0005)
AcctAge
0.007** (.002)
0.037***(.005)
0.033***(.001)
Log likelihood
-13547.45
-3122.35
-29001.21
0 < *** < 0.0011.503
< ** < 0.01 < *2.520
< 0.05 <
DegeneracySignif.
value codes: 1.370
SONIC
-23387.15
+
Advancing the Science of
Networks in Communities
< 0.1
6.672
Results 2:Temporal Proximity
Hypotheses
Variables
Partner
Model P2
IM
Model I2
Trade
Model T2
Mail
Model M2
H1: Sparsity
Edges
-6.435***(.03)
-6.539***(.20)
-5.597***(.04)
-5.218***(.03)
H2: Popularity
GWDegree
1.393***(.19)
-0.884***(.08)
-1.274***(.05)
H3: Transitivity
GWESP
1.393***(.07)
1.224***(.04)
H7: Gender homophily
Same Gender
-0.159***(.02)
-0.134***(.04)
-0.068***(.01)
-0.062***(.010)
H8: Age homophily
Age Difference
-0.034***(.001)
-0.026***(.002)
-0.026***(.001)
-0.030***(.001)
H9: Experience homophily
AcctAge Difference
-0.115***(.005)
-0.059***(.01)
-0.147***(.003)
-0.085***(.003)
H4,4a,4b: Geographical proximity
Log(Distance)
H5,6: Temporal proximity
Timezone difference
-0.226***(.01)
-0.111** (.04)
-0.173***(.02)
-0.153***(.005)
Control
Female
-0.165***(.02)
-0.079* (.03)
-0.040***(.01)
0.029** (.01)
Supported: Time zone
Age difference
0.005***(.0002) -0.002***(.0004)
reduces the likelihood of
AcctAge
0.012***(.002)
0.035***(.005)
0.032***(.001)
interaction, e.g individuals in the
likelihood
-13582.8
-3120.64
-28921.29
-23271.95
same time zone areLog1.25
times
more likely to be partners
than
SONIC
Degeneracy value
0.427
1.224
2.850
5.857
the individuals with one hour
Signif. codes: 0 < *** < 0.001 < ** < 0.01 < * < 0.05 < + < 0.1
difference.
Advancing the Science of
Networks in Communities
Results 3: Geographical Proximity
Hypotheses
Variables
Partner
Model P3
IM
Model I3
Trade
Model T3
Mail
Model M3
H1: Sparsity
Edges
-1.759***(.04)
-5.251***(.22)
-1.848***(.07)
-2.211***(.03)
H2: Popularity
GWDegree
1.958***(.02)
-0.710***(.01)
-1.209***(.05)
H3: Transitivity
GWESP
1.027***(.08)
1.073***(.05)
H7: Gender homophily
Same Gender
-0.196***(.02)
-0.172***(.03)
-0.062***(.01)
-0.074***(.007)
H8: Age homophily
Age Difference
-0.030***(.002)
-0.025***(.002)
-0.023***(.001)
-0.029***(.001)
H9: Experience homophily
AcctAge Difference
-0.107***(.003)
-0.050***(.01)
-0.115***(.003)
-0.084***(.003)
H4,4a,4b: Geographical proximity
Log(Distance)
-1.642***(.007)
-0.999***(.07)
-1.315***(.03)
-1.065***(.002)
H5,6: Temporal proximity
Timezone difference
Control
Female
-0.217***(.02)
-0.086** (.03)
-0.054***(.01)
0.035***(.01)
Supported: Distance reduces
the
Age e.g
0.003***(.0004) -0.003***(.0004)
likelihood of interaction,
individuals 10 Km away AcctAge
from each
0.011***(.003)
0.026***(.005)
other are 5 times more likely to
Log100
likelihood
-12562.75
-3245.12
-27563.39
-22512.26
be partners than the ones
Km
apart. Distance has a stronger
SONIC
Degeneracy value
2.65
1.992
5.171
4.851
impact on partner than IM. Signif. codes: 0 < *** < 0.001 < ** < 0.01 < * < 0.05 < + < 0.1
Advancing the Science of
Networks in Communities
Results 3a: Detail Distance
Hypotheses
Variables
Partner
Model P3a
IM
Model I3a
Trade
Model T3a
Mail
Model
M3a
H1: Sparsity
Edges
-6.799***(.03)
-7.592***(.53)
-6.053***(.08)
-5.547***(.03)
H2: Popularity
GWDegree
1.430***(.33)
-0.793***(.10)
-1.231***(.05)
H3: Transitivity
GWESP
1.051***(.08)
1.058***(.04)
H7: Gender homophily
Same Gender
-0.176***(.02)
-0.170***(.03)
-0.066***(.01)
-0.059***(.01)
H8: Age homophily
Age Difference
-0.032***(.001)
-0.026***(.002)
-0.025***(.001)
-0.030***(.001)
H9: Experience homophily
AcctAge Difference
-0.110***(.005)
-0.044***(.01)
-0.145***(.004)
-0.084***(.003)
H4a: Short distance
Short distance
3.332***(.05)
2.493***(.17)
3.090***(.16)
2.773***(.04)
Intermediate distance
0.215***(.03)
0.151 (.11)
0.218* (.09)
0.162***(.02)
-0.201***(.02)
-0.094***(.03)
-0.042** (.02)
0.029***(.008)
0.004***(.0002)
-0.002***(.0004)
0.012***(.002)
0.028***(.004)
0.032***(.002)
-12783.63
-3237.30
-27781.24
-22668.67
3.319
0.600
8.883
7.596
Control
Female
Short
distance has the strongest
impact compared to intermediate
Age
and long distance, e.g individuals
AcctAge
living within 50 Km are 22.6
times
more likely to be partners
Logthan
likelihood
those who live between 50 and
Degeneracy value
800 Km.
Signif. codes: 0 < *** < 0.001 < ** < 0.01 < * < 0.05 < + < 0.1
SONIC
Advancing the Science of
Networks in Communities
Hypotheses Tested
Hypotheses
Partnershi
p
IM
Trade
Mail
H1
Sparsity
Yes
Yes
Yes
Yes
H2
Popularity
Yes
Yes
No
No
H3
Transitivity
Yes
Yes
N/A
N/A
H4
Geographic proximity
Yes
Yes
Yes
Yes
H4a
Short distance
Yes
Yes
Yes
Yes
H4b
Interaction types
High
Low
Medium
Medium
H5
Temporal proximity
Yes
Yes
Yes
Yes
H6
Synchronization
High
Low
Medium
Medium
H7
Gender homophily
No
No
No
No
H8
Age homophily
Yes
Yes
Yes
Yes
H9
Experience homophily
Yes
Yes
Yes
Yes
SONIC
Advancing the Science of
Networks in Communities
Results and Discussion





Sparsity and transitivity exists in all online relations.
Homophily of age and game experience is supported
in all fours relations.
Distance matters but short distances are more
important.
Time zone has a bigger impact on partner and trade
relations compared to mail and IM relations.
Gender homophily is not supported for all relations
and female players are more likely to interact with the
male players.
SONIC
SONIC
Advancing the Science of
Networks
in Communities
Advancing
the Science o
Networks in Communitie
Jaideep Srivastava
Muhammad A Ahmad
Nishith Pathak
VWE COMPUTATIONAL
CHALLENGES
Outline




Data Extraction and Inference
Algorithm Development Issues
Computational Metrics
Current Work and Future Directions
Data Extraction & Inference

WVE data:
◦ 23 tables, 500 classifications of actions
◦ 3 Terabytes of data
◦ Data captured at the level of actions

Analysis & extraction of higher order
structures
◦ Social Groups
◦ Reconstruction of Events
◦ Group formation and dissipation
Computational Challenges



Challenges in large scale data handling
Need to develop One pass algorithms
Incremental Algorithms
Computational Metrics

Quantitative measurement of
◦ Trust, Performance, Expertise


Quantifying subjective qualities
Inference through
◦ Group membership
◦ Histories of interaction
◦ Co-location and other spatio-temporal
analysis.
Inferring relations from data
Applications



Why do we need to develop such metrics?
The availability of massive amounts of data
makes quantification of otherwise
subjective tasks possible
Hypothesis testing
◦ e.g., High performance groups and individuals
tend to group together

Quantifying and measuring performance of
different tasks
Difficulty Indexes

Factors effecting performance and
assessment of task difficulty
◦
◦
◦
◦
Player Level
Player Effective Level
Group Level
Group Size
◦ Also, task difficulty will play a large role
Monster Composite Difficulty Index
Composite Difficulty vs Avg Pts
3000
2500
1500
1000
500
38
5
40
3
41
9
43
6
45
5
47
2
48
8
50
5
51
9
53
4
54
8
56
6
58
7
61
9
86
11
5
13
2
14
7
16
4
17
9
19
5
20
9
22
5
23
9
25
4
26
8
28
7
30
5
32
2
33
6
35
3
36
8
0
Composite Difficulty
Composite Difficulty vs Avg Pts
500
450
400
350
Avg Pts
15
62
Avg Pts
2000
300
250
200
150
100
50
0
15
25
35
42
43
44
45
51
52
53
54
55
56
62
63
64
65
Composite Difficulty
66
67
73
74
75
82
83
84
85
86
92
93
Experience Rate


Experience gained does not capture
everything about performance
Issues
◦ Relative difficulty varies for player levels
◦ Players can “mentor down”

Proposed metric
DP = Ding points i.e., points needed to move from one level to another.
N = Maximum levels
Experience Rate
1545
957
792
716
638
591
547
505
478
451
424
403
379
357
337
316
295
272
251
231
209
190
171
152
133
114
95
76
57
38
19
0
Frequency
Experience Rate
Distribution of Experience Rate
2500
2000
1500
1000
500
0
General Performance Rate

Performance of players varies:
◦
◦
◦
◦
Size of groups
Type of players grouped with
Difficulty of task
History of play
Churn Prediction Problem

Given player attributes and history of play
◦ Predict which players are likely to quit


Base model: Regression
Proposed models: Network models
◦ Diffusion models
◦ Other classification models
Pattern Mining for Studying Group
Evolution



What are the frequent configurations of changes
in an evolving social network?
How stable are groupings of users across an
extended period of time?
Are there any behavioral patterns that trigger
changes in the topology of the network?
Conclusions

A wide range of findings about the game world, ranging from
economics to network signatures.

New linkages between computer science and social science

Developing new metrics and new algorithms

Began to explore the potential of mapping

Several more studies in process: problematic use, flow, social
capital formation

Still only using about 1/10th of the data