an analysis of social networks analysis in online and face-to-face bridge communities

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LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities

An Analysis of Social Networks Analysis in Online and Face-to-Face Bridge Communities

Alexandru Iosup

Parallel and Distributed Systems Group

Delft University of Technology

Vlad Posea, Mihaela Balint, Alexandru DimitriuPolitehnica University of Bucharest, Romania

Presented by Dick Epema. (Many thanks from the BridgeHelper team.)

LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities2

What’s in a name?

1. Virtual worldExplore, do, learn, socialize, compete+

2. ContentGraphics, maps, puzzles, quests, culture+

3. Game analyticsPlayer stats and relationships

Massively Social Gaming(online) games with massive numbers of players (100K+), for which social interaction helps the gaming experience


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.


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


FarmVille, a Massively Social Game

Key advantage over market:Use [Social Network] analysis to improve gameplay experience Zynga CTO

Sources: CNN, Zynga, 2010.

Source: InsideSocialGames.com


Agenda

1. Background on Massively Social Gaming2. Bridge, the Running Example3. Research Question4. Addressing the Research Question5. Conclusion


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


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


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. Unique type of social network? (new knowledge)2. Unique type of social gaming network? (new knowledge)3. Use results to develop new services (matchmaking, rating)4. Use results to improve online game operations (player

retention)5. “Real-world” applications: other social network results

applied in economics; adversarial settings good for management and psychology studies; etc.


Agenda

1. Background on Massively Social Gaming2. Bridge, the Running Example3. Research Question4. Addressing the Research Question• Method• Data• Analysis Results

5. Conclusion


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


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 Balint, Alexandru Dimitriu, and Alexandru Iosup, An Analysis of the BBO

Fans online social gaming community, RoEduNet International Conference (RoEduNet), 2010 9th.


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 Balint, Alexandru Dimitriu, and Alexandru Iosup, An Analysis of the BBO

Fans online social gaming community, RoEduNet International Conference (RoEduNet), 2010 9th.


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)


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


3. Transform Data into Social Links

Results of Transformation• Method

• Different criteria + thresholds• Validate for Locomotiva using

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+)

Non-isolated nodes

# of communitiesMean community

size

Maximum modularity


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


• Community Builderplays many hands withmany other players

• Community Memberplays mostly with a few community members

• Faithful Player1-2 stable partners

• Random Playerno stable partner

Goal for the future:Reduce # of random players in Face-to-Face

bridge

4. Analysis of G

Player Types


Agenda

1. Background on Massively Social Gaming2. Bridge, the Running Example3. Research Question4. Addressing the Research Question5. Conclusion


Current TechnologyCurrent Technology

The FutureThe Future• Scalability, efficiency• Happy players

• Million-users, multi-bn. market• Content, World Sim, Analytics

Massively Social GamingMassively Social Gaming

• Complete game mechanics• Basic social network tools• Makes players unhappy• Many starters quit

Our VisionOur Vision

• Social Network Analysis +Applications = BridgeHelper

Ongoing WorkOngoing Work

• More analysis• Ranking• Matchmaking


Thank you for your attention! Questions? Suggestions? Observations?

Alexandru Iosup

[email protected]://www.pds.ewi.tudelft.nl/~iosup/ (or google “iosup”)Parallel and Distributed Systems GroupDelft University of Technology

- http://www.st.ewi.tudelft.nl/~iosup/research_gaming.html

- http://BridgeHelper.org (soon)

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an analysis of social networks analysis in online and face-to-face bridge communities

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