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Talk 2017 Respawn / Devcom - Social Network Analysis in Games and Communities

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Talk 2017 Respawn / Devcom - Social Network Analysis in Games and Communities

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Talk 2017 Respawn / Devcom - Social Network Analysis in Games and Communities

  1. 1. S C I E N C E * PA S S I O N * T E C H N O L O G Y SOCIAL NETWORK ANALYSIS 
 IN GAMES AND COMMUNITIES J O H A N N A P I R K E R , T U G R A Z , A U S T R I A S U N A U G 1 9 : : R E S PAW N @ D E V C O M 2 0 1 7
  2. 2. JOHANNA PIRKER ▸ Computer Scientist & Software Engineering @Graz University of Technology ▸ Virtual Realities (Maroon) @Massachusetts Institute of Technology ▸ Research & Edu at Institute for Interactive Systems & Data Science, TU Graz ▸ Virtual Realities & Worlds ▸ HCI, E-Learning, UX, Data Analysis (SNA) ▸ GUR Consulting ▸ Games Education (for CS) & Research, Design, Development & Analysis ▸ Website: www.jpirker.com @JOEYPRINK
  3. 3. DATA ANALYTICS IN GAMES ▸ Understanding player behaviour to create better or more innovative social game experiences ▸ Understanding and identifying patterns in game data ▸ -> who is the player? ▸ -> statistics on player behaviour (retention rate, concurrency, ..) ▸ -> social behaviour of players
  4. 4. SOCIAL NETWORK ANALYSIS
  5. 5. SOCIAL NETWORK ANALYSIS ▸ “Strategy for investigating social structures through the use of network and graph theories” ▸ Nodes (actors, people, topics) ▸ Ties / Edges (relationships) ▸ We can model the world around us as networks ▸ To get new information Further reading: jis.sagepub.com/content/28/6/441.short
  6. 6. SIX DEGREES OF SEPARATION ▸ In 1967, Stanley Milgram (social psychologist at Yale & Harvard) conducted the small-world-experiment that is the basis of the “six degrees of separation” concept. ▸ He sent several packages to randomly selected individuals in the US, asking them to forward the package to a target contact person in Boston. The average path length for the received packages was around 5.5. Further reading: en.wikipedia.org/wiki/Small-world_experiment
  7. 7. SIX DEGREES OF SEPARATION ▸ In 2008, a study of Microsoft showed that the average chain of contacts between users of MSN was 6.6 people. ▸ In 2016, Facebook observed an average connection distance between Facebook users of 3.57. Further reading: en.wikipedia.org/wiki/Small-world_experiment
  8. 8. www.tugraz.at n Hier könnte Ihr Logo stehen Social Network Analysis - Applications §  Political Blogs §  Prior to the 2004 U.S. Presidential election April 28, 2015 Christian Gütl, Johanna Pirker - Institute of Information Systems and Computer Media 10 Reading: http://dl.acm.org/citation.cfm?id=1134277
  9. 9. www.tugraz.at n Hier könnte Ihr Logo stehen Social Network Analysis – Applications §  Organizations §  Email delivery at HP labs §  Informal communication April 28, 2015 Christian Gütl, Johanna Pirker - Institute of Information Systems and Computer Media 11 Reading: http://www.cs.princeton.edu/~chazelle/courses/BIB/HubermanAdamic.pdf
  10. 10. www.tugraz.at n Hier könnte Ihr Logo stehen Social Network Analysis – Applications §  Ingredient networks April 28, 2015 Christian Gütl, Johanna Pirker - Institute of Information Systems and Computer Media 12 Reading: http://dl.acm.org/citation.cfm?id=2380757
  11. 11. www.tugraz.at n Hier könnte Ihr Logo stehen Social Network Analysis – Applications §  Romantic relationships in a US high school, §  18 month period §  (sexually transmitted diseases) April 28, 2015 Christian Gütl, Johanna Pirker - Institute of Information Systems and Computer Media 13 Reading: http://www.soc.duke.edu/~jmoody77/chains.pdf
  12. 12. www.tugraz.at n Hier könnte Ihr Logo stehen Wikipedia Network Game April 28, 2015 Christian Gütl, Johanna Pirker - Institute of Information Systems and Computer Media 15 http://thewikigame.com/
  13. 13. www.tugraz.at n Hier könnte Ihr Logo stehen Graph Basics (1) §  Nodes/vertices (actors) §  Edges/link (inter-node relationships) April 28, 2015 Christian Gütl, Johanna Pirker - Institute of Information Systems and Computer Media 19
  14. 14. www.tugraz.at n Hier könnte Ihr Logo stehen Tools for SNA April 28, 2015 Christian Gütl, Johanna Pirker - Institute of Information Systems and Computer Media §  Gephi (all platforms, os) -> Demo §  gephi.org §  R packages for SNA (all platforms, os) §  NodeXL (for Excel, Windows) §  (D3) 33 (http://bost.ocks.org/mike/miserables/ )
  15. 15. SOCIAL NETWORK ANALYSIS IN MULTI-USER GAMES
  16. 16. WHY?!
  17. 17. TYPICAL QUESTIONS ▸ Analyzing individuals: ▸ Who are well connected / important players in a network? ▸ What is the influence of individuals? ▸ Who is the player with the largest reach? ▸ Who are players connecting different player groups?
  18. 18. TYPICAL QUESTIONS ▸ Analyzing groups and communities: ▸ How can we identify groups and communities? ▸ How are players connected with each other? ▸ Are players more engaged by playing along or together? ▸ Are players in groups performing better than players playing on their own? ▸ Do connected players share common interests?
  19. 19. TYPICAL QUESTIONS ▸ Analyzing social dynamics: ▸ How do players connect to other players? ▸ How do players build guilds? ▸ When a player gets an interesting item to share with other players, how far will it get transmitted? ▸ How can we recommend players in PvP matches?
  20. 20. HOW?!
  21. 21. BUILDING PLAYER NETWORKS ▸ Undirected networks (Links are undirected) ▸ Directed networks (Links are directed) ▸ Weighted networks (Links are weighted)
  22. 22. BUILDING PLAYER NETWORKS ▸ Direct relationships: Direct (explicit) interactions between players are identified and used (e.g. in-game messaging, friendships, clan memberships). ▸ Indirect relationships: Relationships also can be identified through indirect (implicit) interactions (playing in same matches or opponent matches, same playing time, same in-game location).
  23. 23. EXAMPLE?!
  24. 24. SOCIAL NETWORK ANALYSIS IN DESTINY ▸ Work with Anders Drachen, André Rattinger, Rafet Sifa, Günter Wallner ▸ www.gamasutra.com/blogs/AndersDrachen/ 20161123/286112/Playing_with_Friends_in_Destiny.php ▸ http://www.rafetsifa.net/wp-content/uploads/2017/02/ Rattinger_et_al_2016_ICEC.pdf 

  25. 25. NETWORK RELATIONSHIP ‣ Player Network ‣ v: players ‣ e: match played together 
 / against each other ‣ undirected, weighted graph ‣ (weight: # matches played together) PLAYER 1 PLAYER 2 PLAYER 3 3 1
  26. 26. SOCIAL NETWORKS IN DESTINY Rattinger, A., Wallner, G., Drachen, A., Pirker, J., & Sifa, R. (2016, September) Integrating and Inspecting Combined Behavioral Profiling and Social Network Models in Destiny,15th International Conference on Entertainment Computing (in press).
  27. 27. PERFORMANCE ANALYSIS ▸ How perform players? ▸ Players playing more often with the same players in teams have a higher success rate
  28. 28. ENGAGEMENT ANALYSIS ▸ How to engage players? ▸ Players playing more often with the same players in teams play more often and longer
  29. 29. RETENTION ANALYSIS ▸ How to keep players engaged? ▸ Identification of important nodes
  30. 30. SOCIAL NETWORK ANALYSIS IN COMMUNITIES
  31. 31. SOCIAL NETWORK ANALYSIS OF THE GLOBAL GAME JAM ▸ Work with Foaad Khosmood, Christian Gütl, Andreas Punz ▸ https://jpirker.com/wp-content/uploads/ 2013/09/2017icgj-global-game.pdf
  32. 32. GLOBAL GAME JAM ▸ “world’s largest game development event taking place around the world at physical locations” ▸ each game uploaded to GGJ website and linked to jammer profiles ▸ -> social interactions ▸ -> international context
  33. 33. DATASET ▸ Dataset crawled from GGJ website ▸ 2014-2016
  34. 34. NETWORK RELATIONSHIP explicit (friend, follow information) vs implicit (shared interests) networks ▸ Jammer Network: describes connections between jammers through the games they have developed together (v= jammer, e = developed games together) ▸ Location Network: demonstrates the connectivity between various locations or nations through (moving) jammers (v = location, e = jammers developed games together) ▸ Game Network: represents a network of all games developed connected through jammers (v = games, e = common jammers in the development process)
  35. 35. NETWORK RELATIONSHIP ‣ Jammer Network ‣ three-year span ‣ v: jammers ‣ e: developed a game together 
 ‣ undirected, weighted graph ‣ (weight: # games developed together) JAMMER 1 JAMMER 2 JAMMER 3 3 1
  36. 36. NETWORK
  37. 37. NETWORK
  38. 38. NETWORK STRUCTURE ▸ Average degree ▸ avg # of connections j2j: 4.335; most 2-6 jammers ▸ almost 1.500 jammers degree of 1 :-(, a few 9+
  39. 39. NETWORK STRUCTURE ▸ Average weighted degree ▸ avg # of weighted connections j2j: 5.515 ▸ likely to work with same people
  40. 40. NETWORK PROPERTIES Degree can be used to predict tasks (e.g. high degree refers to audio engineers)
  41. 41. NETWORK STRUCTURE Bridge!
  42. 42. GOALS • Improve our understanding of the developer engagement and behaviours to improve experience • Find issues to avoid drop-outs at jam events • Find “important” nodes (bridges) and “weak” nodes • Find flaws early and maybe also automatically/ dynamically
  43. 43. IDEAS Collaboration Graph as Engagement Tool Based on the social network measure a new form of social engagement can be created. Similar to the Small World Problem or the Erdos number, the collaboration graph can be used to engage jammers, to collaborate with new jammers, or jammers at different locations. As gamification tools, jammers could be motivated through their ”degree”, or the path length to another person (e.g. a famous game developer, the ”Carmack number”) to collaborate with new jammers. Carmack Number 0 Carmack Number n Carmack Number 1
  44. 44. IDEAS Collaboration Graph as Engagement Tool Based on the social network measure a new form of social engagement can be created. Similar to the Small World Problem or the Erdos number, the collaboration graph can be used to engage jammers, to collaborate with new jammers, or jammers at different locations. As gamification tools, jammers could be motivated through their ”degree”, or the path length to another person (e.g. a famous game developer, the ”Romero number”) to collaborate with new jammers. Romero Number 0 Romero Number n Romero Number 1
  45. 45. IDEAS Collaboration Graph as Engagement Tool Based on the social network measure a new form of social engagement can be created. Similar to the Small World Problem or the Erdos number, the collaboration graph can be used to engage jammers, to collaborate with new jammers, or jammers at different locations. As gamification tools, jammers could be motivated through their ”degree”, or the path length to another person (e.g. a famous game developer, the ”Pirker number”) to collaborate with new jammers. Pirker Number 0 Pirker Number n Pirker Number n-1Pirker Number n-1
  46. 46. FURTHER READINGS ▸ Overview of further relevant readings: ▸ https://jpirker.com/talk-respawn/
  47. 47. gameconf.org
  48. 48. THANK YOU FOR YOUR ATTENTION. JOHANNA PIRKER, JPIRKER@MIT.EDU, @JOEYPRINK 
 Further information: jpirker.com This is how others play your game!

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