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. 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. 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. SOCIAL NETWORK ANALYSIS

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. 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. 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. www.tugraz.at n
Hier könnte Ihr
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Six Degrees of Separation
April 28, 2015
Christian Gütl, Johanna Pirker - Institute of Information Systems and Computer Media
8

9. www.tugraz.at n
Hier könnte Ihr
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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. 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. 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. 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. SOCIAL NETWORK ANALYSIS OF
THE GLOBAL GAME JAM NETWORK

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. 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. 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. 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. 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. ANALYSIS & RESULTS

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. NETWORK

22. NETWORK

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. NETWORK STRUCTURE
▸ Average weighted degree
▸ avg # of weighted connections
j2j: 5.515
▸ likely to work with same
people

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. 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. NETWORK STRUCTURE
Bridge!

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. 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. 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. 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. 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. 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. 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. THANK YOU FOR YOUR
ATTENTION.
JOHANNA PIRKER, JPIRKER@MIT.EDU, @JOEYPRINK
Further information:
jpirker.com
Thanks to GGJ Foundation!
Thanks to the reviewers!