1. 1 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 Dimitriu Politehnica University of Bucharest, Romania Presented by Dick Epema. (Many thanks from the BridgeHelper team.)

2. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 2 What’s in a name? 1.Virtual world Explore, do, learn, socialize, compete + 2.Content Graphics, maps, puzzles, quests, culture + 3.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

3. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 3 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.

4. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 4 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

5. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 5 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

6. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 6 Agenda 1.Background on Massively Social Gaming 2.Bridge, the Running Example 3.Research Question 4.Addressing the Research Question 5.Conclusion

7. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 7 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

8. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 8 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

9. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 9 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.

10. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 10 Agenda 1.Background on Massively Social Gaming 2.Bridge, the Running Example 3.Research Question 4.Addressing the Research Question Method Data Analysis Results 5.Conclusion

11. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 11 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

12. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 12 1. Gathered Data BBO (Fans): Massively Social Gaming Bridge Base Online (BBO) http://www.bridgebase.comhttp://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/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.

13. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 13 1. Gathered Data Locomotiva: Face-to-Face Bridge Locomotiva http://www.locomotiva.rohttp://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.

14. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 14 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)

15. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 15 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

16. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 16 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 communities Mean community size Maximum modularity

17. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 17 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

18. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 18 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 4. Analysis of G Player Types

19. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 19 Agenda 1.Background on Massively Social Gaming 2.Bridge, the Running Example 3.Research Question 4.Addressing the Research Question 5.Conclusion

20. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 20 Current Technology The Future Scalability, efficiency Happy players Million-users, multi-bn. market Content, World Sim, Analytics Massively Social Gaming Complete game mechanics Basic social network tools Makes players unhappy Many starters quit Our Vision Social Network Analysis + Applications = BridgeHelper Ongoing Work More analysis Ranking Matchmaking

21. LSAP, 2011 – Analysis of Online and Face-to-Face Bridge Communities 21 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 A.Iosup@tudelft.nl http://www.pds.ewi.tudelft.nl/~iosup/ - http://www.st.ewi.tudelft.nl/~iosup/research_gaming.htmlhttp://www.st.ewi.tudelft.nl/~iosup/research_gaming.html - http://BridgeHelper.org (soon)http://BridgeHelper.org More Info: