Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Social Network Analysis of the Global Game Jam Network

491 views

Published on

Presentation at the International Conference on Game Jams in San Francisco 2017

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Social Network Analysis of the Global Game Jam Network

  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 OF THE GLOBAL GAME JAM NETWORK 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 F O A A D K H O S M O O D , C A L P O LY, C A C H R I S T I A N G Ü T L , C U R T I N U N I V E R S I T Y, A U S T R A L I A F E B - 2 6 : : I C G J 2 0 1 7 , S A N F R A N C I S C O
  2. 2. JOHANNA PIRKER ▸ Computer Scientist & Software Engineering @Graz University of Technology ▸ Virtual Worlds @Massachusetts Institute of Technology ▸ Researcher at Institute for Information Systems & Computer Media, TU Graz ▸ Virtual, Immersive Realities & Worlds ▸ HCI, E-Learning, UX, Data Analysis (SNA) ▸ Games Education (for CS) & Research, Design, Development & Analysis ▸ Website: www.jpirker.com @JOEYPRINK
  3. 3. DATA ANALYTICS ▸ Understanding jammer behaviour to create better or innovative jam experiences ▸ Understanding and identifying patterns in jammer data ▸ -> who is the jammer? ▸ -> statistics on jam behaviour (retention rate, concurrency, ..) ▸ -> social behaviour of jammers?
  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 Six Degrees of Separation April 28, 2015 Christian Gütl, Johanna Pirker - Institute of Information Systems and Computer Media 8
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. S O C I A L N E T W O R K S I N D E S T I N Y 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).
  13. 13. SOCIAL NETWORK ANALYSIS OF THE GLOBAL GAME JAM NETWORK
  14. 14. MAIN CONTRIBUTION ▸ First construction of social jammer networks within Global Game Jam ▸ Basic social network analysis of the jammer network ▸ Discussion of potential social networks for the GGJ and future game jam research
  15. 15. 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
  16. 16. DATASET ▸ Dataset crawled from GGJ website ▸ 2014-2016 ▸ registered jam sites (1.637 dp) ▸ location, country, jammers, uploaded games ▸ jammer entries (85.387 dp) ▸ locations, skills, uploaded games ▸ uploaded games (15.955 dp) ▸ name, platforms, tools, description ▸ Data set pre-processing (cleaning): ▸ names replaced by IDs ▸ only jammers who submitted required information (18.426)
  17. 17. 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)
  18. 18. 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
  19. 19. ANALYSIS & RESULTS
  20. 20. GOAL ▸ look at subset of network to get an understanding of the structure in the GGJ network ▸ identify potential future research possibilities with this data ▸ identify potential of graphs as tool
  21. 21. NETWORK
  22. 22. NETWORK
  23. 23. NETWORK STRUCTURE ▸ Average degree ▸ avg # of connections j2j: 4.335; most 2-6 jammers ▸ almost 1.500 jammers degree of 1 :-(, a few 9+
  24. 24. NETWORK STRUCTURE ▸ Average weighted degree ▸ avg # of weighted connections j2j: 5.515 ▸ likely to work with same people
  25. 25. NETWORK STRUCTURE ▸ Of 39,939 edges, which represent the collaboration through games, ▸ 8,631 (21.61%) collaborate more than one time with the same jammer, ▸ 1,998 (0.50%) more than twice, and ▸ 233 (0.58%) more than 3 times. ▸ 29 (0.07%) even worked together 4 or more times with the same partner (e.g. working on two games at the same jam).
  26. 26. NETWORK STRUCTURE ▸ Largest Component (LCC) ▸ very small! ▸ 56 nodes, 194 edges ▸ Game jam location in Brazil ▸ avg degree: 6.929 ▸ weighted degree: 12.786 ▸ Many of these jammers have participated every year, some have even worked on more than one game per year.
  27. 27. NETWORK STRUCTURE Bridge!
  28. 28. GOALS • Improve our understanding of the jammer engagement and behaviours to improve experience • Find issues to avoid drop-outs • Find “important” nodes (bridges) and “weak” nodes • Find flaws early and maybe also automatically/ dynamically
  29. 29. OBSERVATIONS • Jammer graph is not connected: several subgraphs representing the location, and barely connected to other sites • Degree can be used to predict tasks (e.g. high degree refers to audio engineers) • Network metrics can be used to correlate with aspects such as retention rates or engagement (future work)
  30. 30. IDEAS Social Networks to Strengthen the Community: Social network can be used to identify weak ties and important nodes, which are ”bridges” or ”hubs” and are connecting many other nodes and components. is knowledge can be useful to avoid drop-outs. Additionally, SNA can be used to identify ”strong” nodes (with a high degree). These nodes are often very experienced with game jams and can be used as ”experts” to create new jam sites or to help organizing events, or as part of a group, which is not very experienced yet.
  31. 31. IDEAS Social Networks for Dynamic Group Formation Graph-based recommendation tools are already popular in various fields, such as games, recipes and products. Forming proper and meaningful groups can be a struggle in large game jam settings, as no widely adopted methods exist. Using social networks in a tool for group formation could be a fast, easy, and interesting replacement of traditional methods.
  32. 32. 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
  33. 33. 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
  34. 34. 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
  35. 35. THANK YOU FOR YOUR ATTENTION. JOHANNA PIRKER, JPIRKER@MIT.EDU, @JOEYPRINK 
 Further information: jpirker.com Thanks to GGJ Foundation! Thanks to the reviewers!

×