Network analysis shows previously unreported features of Javanese traditional theatre
#DH2018, Mexico City

Miguel Escobar Varela (@miguelJogja) and Andrew Schauf
National University of Singapore

share this presentation: miguelescobar.com/dh2018
versión en Español: miguelescobar.com/dh2018/es.html

Wayang kulit is one of the most important theatre traditions of Southeast Asia

Wayang has been part of Java for more than a thousand years.

Background image: Balitung inscription, year 907
Stories used in wayang originate in India (most notably the Mahabharata), but have been transformed over centuries in Java.
Image credit: Gunawan Kartapranata

Network analysis of wayang kulit stories

  • We built a digital repository of storylines from an authoritative list (Purwadi, 2000)
  • We constructed a weighted, undirected co-occurence network of characters at the adegan (scene) level
  • Characters as nodes, edge between two characters means they appear together in at least one scene
  • Edge weight indicates the number of scenes in which both characters appear
Figure 1. Network visualization of correlations between wayang kulit characters at the scene level.

The network at first glance

  • Ubiquitous characteristics of real-life social networks (Carrington, Scott, and Wasserman 2005; Knoke and Yang 2008):
    • High heterogeneity
    • Low clustering coefficient (0.863)
    • Low average shortest path (0.86)
Figure 2. Log-log plot of stories per character in the wayang network. The solid lines represent the actual distribution and the dashed line the theoretical power-law distributions.

Unexpected findings

Finding #1. Although the percentage of Javanese and Indian characters is comparable, Javanese characters have disproportionately lower degrees.

PercentageAverage Weighted Degree
Indian Characters53%153.47
Javanese Characters47%73.36

Table 1. Comparison of Indian and Javanese characters
Comparison Javanese and Indian characters

Figure 3. The different characters according to their weighted degree. The x axis is in an exponential scale (factor = 0.3). Black circles are Javanese, white are Indian and gray are Javanese Punokawan.

Finding #2. Characters with lower degrees are interchangeable in performance, high degree characters are not.

Permissible changes

Figure 4. The different characters according to their weighted degree. The x axis is in an exponential scale (factor = 0.3). Black circles are those where puppet interchange is permissible and white circles are those where it is not.

Interactive online portal



  • We hope to contribute a Southeast Asian case study to the growing area of network analysis of drama.
  • We hope to show how network analysis can contribute to the study of wayang kulit.
Thank you.
Agarwal, Apoorv, Augusto Corvalan, Jacob Jensen, and Owen Rambow. 2012. “Social Network Analysis of Alice in Wonderland.” In CLfL@ NAACL-HLT, 88–96.
Bollen, Jonathan. 2017. “Data Models for Theatre Research: People, Places, and Performance.” Theatre Journal 68 (4):615–32. https://doi.org/10.1353/tj.2016.0109.
Carrington, Peter J., John Scott, and Stanley Wasserman. 2005. Models and Methods in Social Network Analysis. Vol. no. 28. Structural Analysis in the Social Sciences. Cambridge: Cambridge University Press.
Choi, Yeon-Mu, and Hyun-Joo Kim. 2007. “A Directed Network of Greek and Roman Mythology.” Physica A: Statistical Mechanics and Its Applications 382 (2):665–71.
Elson, David K, Nicholas Dames, and Kathleen R McKeown. 2010. “Extracting Social Networks from Literary Fiction.” In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 138–47. Association for Computational Linguistics.
Escobar Varela, Miguel. 2017. “From Copper-Plate Inscriptions to Interactive Websites: Documenting Javanese Wayang Theatre.” In Documenting Performance: The Context and Processes of Digital Curation and Archiving, 203–14. London and New York: Bloomsbury Methuen Drama.
Fischer, Frank, Mathias Göbel, Dario Kampkaspar, Christopher Kittel, and Peer Trilcke. 2017. “Network Dynamics, Plot Analysis: Approaching the Progressive Structuration of Literary Texts.” In Digital Humanities 2017 (Montréal, 8--11 August 2017). Book of Abstracts.
Knoke, David, and Song Yang. 2008. Social Network Analysis. Vol. 154. Quantitative Applications in the Social Sciences. Los Angeles: SAGE.
Moretti, Franco. 2011. “Network Theory, Plot Analysis.” New Left Review, no. 68(March):80.
Park, Gyeong-Mi, Sung-Hwan Kim, Hye-Ryeon Hwang, and Hwan-Gue Cho. 2013. “Complex System Analysis of Social Networks Extracted from Literary Fictions.” International Journal of Machine Learning and Computing 3 (1):107.
Pohl, Mathias, Florian Reitz, and Peter Birke. 2008. “As Time Goes by: Integrated Visualization and Analysis of Dynamic Networks.” In Proceedings of the Working Conference on Advanced Visual Interfaces, 372–75. ACM.
Purwadi. 2009. Kempalan Balungan Lakon Wayang Purwa. Surakarta: Cendrakasih.
Trilcke, Peer, Frank Fischer, and Dario Kampkaspar. 2015. “Digital Network Analysis of Dramatic Texts.” In DH2015 Conference Abstracts. Sydney, Australia. Vol. 1184.
Waumans, Michaël C, Thibaut Nicodème, and Hugues Bersini. 2015. “Topology Analysis of Social Networks Extracted from Literature.” PloS One 10 (6):e0126470.
Xanthos, Aris, Isaac Pante, Yannick Rochat, and Martin Grandjean. 2016. “Visualising the Dynamics of Character Networks.” In Digital Humanities 2016: Conference Abstracts, 417–19.