An Analysis of Social Networks
Analysis in Online and Face-to-Face
Bridge Communities
Alexandru Iosup
Vlad Posea, Mihaela Balint,
Alexandru Dimitriu
Politehnica University of Bucharest,
Romania
Parallel and Distributed Systems Group
Delft University of Technology
Presented by Dick Epema.
(Many thanks from the BridgeHelper team.)
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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What’s in a name?
Massively Social Gaming
(online) games with massive
numbers of players (100K+),
for which social interaction
helps the gaming experience
1. Virtual world
Explore, do, learn,
socialize, compete
+
2. Content
Graphics, maps,
puzzles, quests, culture
+
3. Game analytics
Player stats and
relationships
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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MSGs are a Popular, Growing Market
• 25,000,000 subscribed players (from 150,000,000+ active)
• Over 10,000 MSGs in operation
• Market size 7,500,000,000$/year
Sources: MMOGChart, own research.
Sources: ESA, MPAA, RIAA.
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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Social Networks: Buzzword? Science?
• Social Network=undirected graph, relationship=edge
• Community=sub-graph, density of edges between its
nodes higher than density of edges outside sub-graph
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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FarmVille, a Massively Social Game
Sources: CNN, Zynga, 2010.
Key advantage over market:
Use [Social Network] analysis
to improve gameplay
experience Zynga CTO
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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Source: InsideSocialGames.com
Agenda
1.
2.
3.
4.
5.
Background on Massively Social Gaming
Bridge, the Running Example
Research Question
Addressing the Research Question
Conclusion
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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Bridge, A Traditional Team Card Game
• Bridge as traditional card game
• Hand=one “game”
• 2 pairs (4 players) play
hands (bidding + play)
• Duplicate bridge
•
•
•
•
Team=2 pairs at separate tables
Same hand at every table
Same team plays opposite ends
Eliminates luck
• Only team game at last World
Mind Sport Games, Beijing, 2008
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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Bridge, a Special Use Case of SocNets?
• Similarities
• Online and Face to Face
• Complex agreements between partners (like a social partnership)
• A good pair forms in a very long period of time (like a social …)
• Differences
• Adversarial context, not only cooperation and ‘friendship’
• Gaming social networks have no strict definition
of relationship (‘played once’ vs ‘day-to-day partner’)
• Links in the network not specified precisely
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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Research Question: What are the
Characteristics of Bridge Communities?
•
Study the activity and socnet characteristics of
online and face-to-face bridge communities
•
Why is this interesting?
1.
2.
3.
4.
5.
Unique type of social network? (new knowledge)
Unique type of social gaming network? (new knowledge)
Use results to develop new services (matchmaking, rating)
Use results to improve online game operations (player retention)
“Real-world” applications: other social network results applied in
economics; adversarial settings good for management and
psychology studies; etc.
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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Agenda
1.
2.
3.
4.
Background on Massively Social Gaming
Bridge, the Running Example
Research Question
Addressing the Research Question
•
•
•
Method
Data
Analysis Results
5. Conclusion
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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Analysis of BBOFans
Method
1. Gather data from online and face-to-face communities
•
Data: who played with or against whom, and when?
2. Analyze player activity levels [see article]
3. Transform the play data into G=(V,E),
V=set of players, E=set of social relations.
•
Investigate social relations based on play relationships
4. Analyze properties of graph G
•
•
•
Traditional socnet analysis, e.g., community detection
Player type analysis
Use face-to-face data to guide analysis of online data
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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1. Gathered Data
BBO (Fans): Massively Social Gaming
• Bridge Base Online (BBO) http://www.bridgebase.com
•
•
•
•
Largest online bridge platform, free to play
1M active players, also attracts many professional players
Friends and enemies, filtering by skill and nationality
No advanced social networking features, e.g.,
No Friends-of-Friends
• BBO Fans http://www.bbofans.com/
• Uses BBO for actual gameplay
• BBO Fans community included in BBO
• Better social network facilities
• Community tools: awards, ranking, rated tournaments, etc.
Vlad Posea,
Mihaela
Dimitriu,
LSAP, 2011
– AnalysisBalint,
of Online andAlexandru
Face-to-Face Bridge
Communities and Alexandru Iosup,
An Analysis of the BBO Fans online social gaming community,
RoEduNet International Conference (RoEduNet), 2010 9th.
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1. Gathered Data
Locomotiva: Face-to-Face Bridge
• Locomotiva http://www.locomotiva.ro
•
•
•
•
•
•
Typical of many large clubs around the world [see article]
Large bridge community, free to play
~275 active players, also attracts many top players
4 tournaments per week, 15 bigger tournaments per year
20-60 people per tournament, ~4h/tournament
Games/Tournaments recorded as participants and results
Vlad Posea,
Mihaela
Dimitriu,
LSAP, 2011
– AnalysisBalint,
of Online andAlexandru
Face-to-Face Bridge
Communities and Alexandru Iosup,
An Analysis of the BBO Fans online social gaming community,
RoEduNet International Conference (RoEduNet), 2010 9th.
13
1. Gathered Data
Datasets
•
Face-to-face bridge data
•
•
•
Created real-world club management software
Locomotiva data
Online bridge data
•
•
Created domain-specific web crawler
BBO + BBO Fans data (BBO Fans included in BBO)
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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3. Transform Data into Social Links
What is a Link? A New Framework
• Main idea: Two players have a social relationship
if they relate strongly through play
• They are at the same place at the same time
• They have played together or against each other
• A number of hands
• A number of sessions (all hands in one sitting)
• They are part of the same team
• Can extract social relationships from our datasets
• Single criteria + thresholds
• Multi-creteria + multiple thresholds
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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3. Transform Data into Social Links
Results of Transformation
• Method
Maximum modularity
• Different criteria + thresholds
Mean community size
• Validate for Locomotiva using
# of communities
Non-isolated nodes
human experts (from the club)
• Present extracted
communities to expert
• +1 if regular partners in
same community, etc.
• Validated validators via
maximum modularity (Q)
• (P+>=200) OR (S+>=8)
• Played hands as partners (P+)
• Sessions as partners (S+)
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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3. Transform Data into Social Links/4. Analysis of G
Normalization + Analysis results
• Normalization
•
•
•
•
Threshold values valid for a given community size
Played hands and sessions are cumulative in # of weeks
For Locomotiva: 50 weeks
For BBO: 5 weeks
• For BBO
• P+ >= 20 (200 x 5 / 50)
• Obtained modularity Q = 0.43 (same as for Locomotiva)
• 4,375 communities, 90% of which have at most 4 players
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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4. Analysis of G
Player Types
• Community Builder
plays many hands with
many other players
• Community Member
plays mostly with a few
community members
• Faithful Player
1-2 stable partners
• Random Player
no stable partner
Goal for the future:
Reduce # of random players in Face-to-Face bridge
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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Agenda
1.
2.
3.
4.
5.
Background on Massively Social Gaming
Bridge, the Running Example
Research Question
Addressing the Research Question
Conclusion
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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Massively Social Gaming
• Million-users, multi-bn. market
• Content, World Sim, Analytics
•
•
•
•
Current Technology
Our Vision
Complete game mechanics
Basic social network tools
Makes players unhappy
Many starters quit
• Social Network Analysis +
Applications = BridgeHelper
Ongoing Work
• More analysis
• Ranking
• Matchmaking
The Future
• Scalability, efficiency
• Happy players
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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Thank you for your attention!
Questions? Suggestions? Observations?
More Info:
- http://www.st.ewi.tudelft.nl/~iosup/research_gaming.html
- http://BridgeHelper.org (soon)
Alexandru Iosup
[email protected]
http://www.pds.ewi.tudelft.nl/~iosup/ (or google “iosup”)
Parallel and Distributed Systems Group
Delft University of Technology
LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities
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