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

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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. - PowerPoint PPT Presentation

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  • An Analysis of Social Networks Analysis in Online and Face-to-Face Bridge CommunitiesAlexandru IosupParallel and Distributed Systems Group Delft University of TechnologyVlad Posea, Mihaela Balint, Alexandru DimitriuPolitehnica University of Bucharest, RomaniaPresented by Dick Epema. (Many thanks from the BridgeHelper team.)

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  • Whats in a name?

    Virtual world Explore, do, learn, socialize, compete +Content Graphics, maps, puzzles, quests, culture +Game analytics Player stats and relationships

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

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  • MSGs are a Popular, Growing Market25,000,000 subscribed players (from 150,000,000+ active) Over 10,000 MSGs in operation Market size 7,500,000,000$/year

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  • Social Networks: Buzzword? Science?Social Network=undirected graph, relationship=edgeCommunity=sub-graph, density of edges between its nodes higher than density of edges outside sub-graph

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  • FarmVille, a Massively Social GameKey advantage over market: Use [Social Network] analysis to improve gameplay experience Zynga CTO

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  • AgendaBackground on Massively Social GamingBridge, the Running ExampleResearch QuestionAddressing the Research QuestionConclusion

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  • Bridge, A Traditional Team Card GameBridge as traditional card gameHand=one game2 pairs (4 players) play hands (bidding + play)

    Duplicate bridge Team=2 pairs at separate tables Same hand at every tableSame team plays opposite endsEliminates luck Only team game at last World Mind Sport Games, Beijing, 2008

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  • Bridge, a Special Use Case of SocNets?SimilaritiesOnline 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 )

    DifferencesAdversarial context, not only cooperation and friendshipGaming social networks have no strict definition of relationship (played once vs day-to-day partner)Links in the network not specified precisely

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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?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.

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  • AgendaBackground on Massively Social GamingBridge, the Running ExampleResearch QuestionAddressing the Research QuestionMethodDataAnalysis ResultsConclusion

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  • Analysis of BBOFans MethodGather data from online and face-to-face communitiesData: who played with or against whom, and when?Analyze player activity levels [see article]Transform the play data into G=(V,E), V=set of players, E=set of social relations.Investigate social relations based on play relationshipsAnalyze properties of graph GTraditional socnet analysis, e.g., community detectionPlayer type analysisUse face-to-face data to guide analysis of online data

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  • 1. Gathered DataBBO (Fans): Massively Social GamingBridge Base Online (BBO) http://www.bridgebase.com Largest online bridge platform, free to play1M active players, also attracts many professional playersFriends and enemies, filtering by skill and nationalityNo advanced social networking features, e.g., No Friends-of-Friends BBO Fans http://www.bbofans.com/ Uses BBO for actual gameplayBBO Fans community included in BBOBetter social network facilitiesCommunity 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.

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  • 1. Gathered DataLocomotiva: Face-to-Face BridgeLocomotiva 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 players4 tournaments per week, 15 bigger tournaments per year20-60 people per tournament, ~4h/tournamentGames/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.

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  • 1. Gathered DataDatasetsFace-to-face bridge dataCreated real-world club management softwareLocomotiva dataOnline bridge dataCreated domain-specific web crawlerBBO + BBO Fans data (BBO Fans included in BBO)

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  • 3. Transform Data into Social LinksWhat is a Link? A New FrameworkMain idea: Two players have a social relationship if they relate strongly through playThey are at the same place at the same timeThey have played together or against each otherA number of handsA number of sessions (all hands in one sitting)They are part of the same team Can extract social relationships from our datasetsSingle criteria + thresholdsMulti-creteria + multiple thresholds

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  • 3. Transform Data into Social LinksResults of TransformationMethodDifferent criteria + thresholdsValidate 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 sizeMaximum modularity

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  • 3. Transform Data into Social Links/4. Analysis of GNormalization + Analysis resultsNormalizationThreshold values valid for a given community sizePlayed hands and sessions are cumulative in # of weeksFor Locomotiva: 50 weeksFor BBO: 5 weeks

    For BBOP+ >= 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

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  • 4. Analysis of G Player TypesCommunity Builder plays many hands with many other playersCommunity Member plays mostly with a few community membersFaithful Player 1-2 stable partnersRandom Player no stable partnerGoal for the future:Reduce # of random players in Face-to-Face bridge

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  • AgendaBackground on Massively Social GamingBridge, the Running ExampleResearch QuestionAddressing the Research QuestionConclusion

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  • Current TechnologyThe FutureScalability, efficiencyHappy playersMillion-users, multi-bn. marketContent, World Sim, AnalyticsMassively Social GamingComplete game mechanicsBasic social network toolsMakes players unhappyMany starters quitOur VisionSocial Network Analysis + Applications = BridgeHelperOngoing WorkMore analysisRankingMatchmaking

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  • Thank you for your attention! Questions? Suggestions? Observations? Alexandru Iosup A.Iosup@tudelft.nl http://www.pds.ewi.tudelft.nl/~iosup/ (or google iosup) Parallel and Distributed Systems Group Delft University of Technology http://www.st.ewi.tudelft.nl/~iosup/research_gaming.html http://BridgeHelper.org (soon) More Info:

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    Fishing only once / cyclic start - stopWorld of Warcraft and Runescape >3,000,000 eachMarket size: similar to movies and music markets, but growing faster.

    Main conclusion: multi-criterion best; good modularity properties (Q is high), top score from human experts analysis

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