Network Dynamics, Plot Analysis
Approaching the Progressive Structuration
of Literary Texts
Frank Fischer¹ · Peer Trilcke² · Mathias Göbel³
Dario Kampkaspar⁴ · Christopher Kittel⁵
¹ Higher School of Economics, Moscow
² University of Potsdam
³ Göttingen State and University Library
⁴ Herzog August Library, Wolfenbüttel
⁵ University of Graz
this presentation: dlina.github.io/presentations/2017-montreal/
DH2017 · Montréal 🇨🇦 · 10 August 2017
TOC
- Introduction
- Network Dynamics – Plot Analysis?
- Dynamic Graph Visualisation
- Calculation of Dynamic Networks
- Dynamics, Rhythmics – A Summary
- Bibliography
1. Introduction
A Look Back to 2011 …
dlina Project
Our Working Group
- F. Fischer (Moscow), M. Göbel (Göttingen), D. Kampkaspar (Wolfenbüttel), C. Kittel (Graz), H.-L. Meiners (Göttingen), D. Skorinkin (Moscow), P. Trilcke (Potsdam)
- our blog: lina.digital
- GitHub: github.com/dlina, github.com/lehkost/dramavis
General Approach
- automated, philologically curated extraction, analysis and interpretation of network data from literary (mainly dramatic) texts
Drama Corpora in TEI
- "Théâtre Classique": 1030 plays (17th and 18th century)
- "Shakespeare His Contemporaries": 510 plays from the Shakespeare era, cf. Mueller 2014
- Dramawebben: 62 Swedish plays
- Russian Drama Corpus: 68 Russian plays (1740–1930)
and …
dlina Project
Corpus
- primary working corpus (under revision): "dlina Corpus 15.07", includes 465 German-language plays (1731–1929), derived from TextGrid repository
- see our blogpost "Introducing ›dlina Corpus 15.07‹"
dlina Project
Network Data
- nodes (characters, dramatis personae)
- edges (interactions between characters)
- operationalisation of "interaction": if two characters perform at least one speech act within a given segment of a play (scene, act), they interact with each other (following Solomon Marcus, "Poetica matematică", 1970)
Preliminaries (1/2)
- abstract models of literary texts based on what formal network analysis calls "structural intuition" (Freeman 2004: 4)
- exploration of the inherent logic and cognitive value of these abstract models
Preliminaries (2/2)
- research questions originate from traditional literary studies, but undergo a considerable abstraction in course of network-analytic operationalisation
- as a consequence, the results we receive wouldn't necessarily reach into discourses of the traditional literary studies
Foretaste:
The ›Beat‹ of a Theatre Play
drama-change-rate chart for Goethe's play "Iphigenia in Tauris" (1787)
2. Network Dynamics – Plot Analysis?
to date, research revolves
around static networks:
static network graph for Lessing's play "Emilia Galotti" (1772)
Network Dynamics
- However, literary texts (and their social networks with them) unfold over time.
- Our goal is to describe the network of a drama in its successive transformation and to compare its individual states.
- Focus on network dynamics/dynamic networks.
Plot Analysis?
- A step towards the unresolved promise by Franco Moretti (pamphlet #2, 2011): Network analysis as plot analysis.
- Temporal dimension of 'plot':
- "the repeated attempts to redefine parameters of plot reflect both the centrality and the complexity of the temporal dimension of narrative" (Dannenberg 2005: 435)
- plot as concept for the description of the "progressive structuration" of texts (Kukkonen 2013: §4)
3. Dynamic Graph Visualisation
An Example
– https://maladesimaginaires.github.io/intnetviz/ –
cf. Xanthos et al.: Visualising the Dynamics of Character Networks (DH2016)
Problems of Graph Visualisation
- rather useful for didactic purposes, but …
- … "too close" for the analysis of larger corpora
4. Calculation of Dynamic Networks
Two Types of Questions
time-oriented (indices etc.)
- identifying a point in time: at which point within the 'progressive structuration' of a text does X happen (e.g., all-in index; central-character-entry index)
- quantifying a point in time: what happens at point Y of a text (e.g., final-scene-size measure)
action-oriented (change rates etc.)
- How can we characterise the (sequence of structural states of the) 'progressive strucutration' of a text? (e.g., drama-change rate)
Time-Oriented:
All-In Index
leading question
- identifying a point in time: when did all characters of a drama occur at least once?
precondition
- normalised determination of a point in time in a play
- realised using the 'progression index'
Time-Oriented:
All-In Index
all-in index for 6 selected plays:
Mean value (orange line) refers to the corpus as a whole
Time-Oriented:
Final-Scene-Size Measure
Action-Oriented:
drama-change rate
- goal: to determine value for the dynamics of 'progressive structuration'
- introducing the drama-change rate
approach: the good old configuration matrix (Solomon Marcus, Manfred Pfister) – reinvented as 'segment matrix'
side note: corpus was limited to plays with more than 5 acts/scenes (left with 331 plays)
Segment Matrix
segment matrix for Goethe's "Iphigenia in Tauris" (1787)
blue box = character is present on stage in a given segment
Segment Matrix
segment matrix for Goethe's "Iphigenia in Tauris" (1787)
orange box = character is present in two subsequent segments (liaison des scenes)
Drama-Change Rate Calculation
segment matrix for first act of Goethe's "Iphigenia in Tauris" (1787)
Drama-Change Rate Calculation
segment matrix for first act of Goethe's "Iphigenia in Tauris" (1787)
Drama-Change Rate Calculation
segment matrix for first act of Goethe's "Iphigenia in Tauris" (1787)
Drama-Change Rate Calculation
segment matrix for first act of Goethe' "Iphigenia in Tauris" (1787)
Iphigenia's "Beat"
drama-change-rate chart for Goethe's "Iphigenia in Tauris" (1787)
drama-change-rate-measures: Mean = 0.598 | SD = 0.163
Iphigenia's "Beat"
drama-change-rate chart for Goethe's "Iphigenia in Tauris" (1787) – orange lines (vertical): acts
drama-change-rate measures: Mean = 0.598 | SD = 0.163
Iphigenia's "Beat"
drama-change-rate chart zu Goethe's "Iphigenia in Tauris" (1787) – orange line (horizontal): Mean
drama-change-rate measures: Mean = 0.598 | SD = 0.163
200 Years of Drama-Change Rates
drama-change-rate Mean for 331 plays
dlina corpus, plays with low number of segments (< 6) left out
200 Years of Drama-Change Rates:
The Middle
drama-change-rate Mean for 331 plays
dlina corpus, plays with low number of segments (< 6) left out
200 Years of Drama-Change Rates:
Outliers
drama-change-rate Mean for 331 plays
plays with low number of segments (< 6) left out
An Upward Outlier (Mean)
drama-change-rate-chart for Goethe's "Egmont" (1788) – orange line (horizontal): Mean
drama-change-rate measures: Mean = 0.993 | SD = 0.023
Other Upward Outliers (Mean)
top left: Goethe's "Götz von Berlichingen" (1773); right: Lenz's "Der Hofmeister" (1774) –
bottom left: Tieck's "Die verkehrte Welt" (1800); right: Grabbe's "Napoleon oder die 100 Tage" (1831)
Downward Outliers (Mean)
top left: Stephanie's "Der Schauspieldirektor" (1786); right: Goethe's "Der Bürgergeneral" (1793) –
bottom left: Benedix's "Die Hochzeitsreise" (1849); right: Rilke's "Ohne Gegenwart" (1898)
How to Read
Drama-Change-Rate Charts
Lesson 1: dynamics → arithmetic mean (Mean)
- upward outliers = highly dynamic plays (a large part or even all characters are exchanged between segments)
- downward outliers = low-dynamic plays (a small part or no characters are exchanged between segments)
Lesson 2: rhythmics → standard deviation (SD)
Upward Outliers (SD)
left: Benkowitz's "Die Jubelfeier der Hölle, der Faust der jüngere" (1801) | SD = 0.334;
right: Goethe's "Faust, pt. I" (1808) | SD = 0.326
Downward Outliers (SD)
left: Schnitzler's "Anatol" (1893) | SD = 0.079;
right: Schnitzler's "Der Reigen" (1902) | SD = 0.026
Intermediate Result:
Two Pairs of Extremes (1/2)
Mean outliers (left: upward, right: downward) – orange line: Mean.
left: Lenz's "Der Hofmeister" (1774), Mean: 0.899;
right: Goethe's "Der Bürgergeneral" (1793), Mean: 0.396.
Intermediate Result:
Two Pairs of Extremes (2/2)
SD outliers (left: upward, right: downward) – orange line: Mean.
left: Benkowitz's "Die Jubelfeier der Hölle, der Faust der jüngere" (1801), SD: 0.334;
right: Schnitzler's "Der Reigen" (1902), SD: 0.026.
Recap: Outliers
drama-change-rate Mean for 331 plays
dlina corpus, plays with low number of segments (< 6) left out
Change of Focus: The Middle
drama-change-rate Mean for 331 plays
dlina corpus, plays with low number of segments (< 6) left out
Drama-Change-Rate Normal Type
drama-change-rate Mean for 331 plays
dlina corpus, plays with low number of segments (< 6) left out
Four Drama-Change-Rate Normal Types
top right: L. A. V. Gottsched's "Die Pietisterey im Fischbein-Rocke" (1736);
left: Schiller's "Wallensteins Tod" (1799) –
bottom right: Ganghofer's "Der Herrgottschnitzer von Ammergau" (1880);
left: Sudermann's "Der Bettler von Syrakus" (1911)
Normal Type 'Beat'
drama-change-rate chart for Ganghofer's "Der Herrgottschnitzer von Ammergau" (1880)
drama-change-rate measures: Mean = 0.646 | SD = 0.229
5. Dynamics, Rhythmics – A Summary
Dynamics, Rhythmics
New Terminology
- ›dynamics‹ of drama-change rate
- ›rhythmics‹ of drama-change rate
Raising the Question …
- … what kind of new understanding of the (historical and typological) phenomenon "drama" could be offered by this terminology
Dynamics, Rhythm
A Final Question
- Could we find perceptual/psychological justifications for the rhythmics of a certain drama-change rate?
- something along these lines:
- The average play (normal type with average rhythmics) answers to basic needs and offers a mixture of dynamisation and consistency (solid entertainment)?
- Contrarily, deviant values for rhythmics (outliers) point to avant-garde-type plays: extreme changes (provocation) or extreme uniformity (boredom)?
Read On
- DLINA blog (lina.digital)
- DLINA data on GitHub
- dramavis on GitHub
- Play(s) app on GitHub
- Twitter:
- #dlina
- Frank Fischer (@umblaetterer)
- Mathias Göbel (@goebel_m)
- Christopher Kittel (@chris_kittel)
- Peer Trilcke (@peertrilcke)
6. Bibliography
Bibliography (1/4)
- Agarwal, A., Corvalan, A., Jensen, J., Rambow, O. (2012). Social Network Analysis of Alice in Wonderland. Proceedings of the Workshop on Computational Linguistics for Literature, Montréal, pp. 88–96, http://www.aclweb.org/anthology/W12-2513.
- Carley, K. M. (2003). Dynamic Network Analysis. Breiger, R., Carley, K. M., Pattison, P. (eds.): Dynamic Social Network Modeling and Analysis. Workshop Summary and Papers. Washington D. C., pp. 133–145, http://www.nap.edu/read/10735/chapter/9.
- Dannenberg, H. (2005). Plot. Herman, D., Jahn, M., Ryan, M.-L. (eds.): The Routledge Encyclopedia of Narrative Theory. London: Routledge, pp. 435–439.
- Fischer, F., Göbel, M., Kampkaspar, D., Trilcke, P. (2015). Digital Network Analysis of Dramatic Texts. Digital Humanities 2015. Conference Abstracts. University of Western Sydney, online.
- de Nooy, W. (2006). Stories, Scripts, Roles, and Networks. Structure and Dynamics 1.2, http://escholarship.org/uc/item/8508h946.
- Elson, D. K., Dames, N., McKeown, K. R. (2010). Extracting Social Networks from Literary Fiction. Proceedings of ACL 2010, Uppsala, pp. 138–147, http://dl.acm.org/ft_gateway.cfm?id=1858696&type=pdf&CFID=659731302&CFTOKEN=83466756.
Bibliography (2/4)
- Federico, P., Aigner, W., Miksch, S., Windhager, F., Zenk, L. (2011). A Visual Analytics Approach to Dynamic Social Networks. Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies (i-KNOW). Graz, http://publik.tuwien.ac.at/files/PubDat_198995.pdf.
- Freeman, L. C. (2004). The Development of Social Network Analysis. A Study in the Sociology of Science. Vancouver: Empirical Press.
- Jannidis, F., Reger, I., Krug, M., Weimer, L., Macharowsky, L., Puppe, F. (2016). Comparison of Methods for the Identification of Main Characters in German Novels. Digital Humanities 2016. Conference Abstracts. Jagiellonian University & Pedagogical University, Kraków, pp. 578–582, http://dh2016.adho.org/abstracts/297.
- Kittel, C., Fischer, F. (2016). dramavis (v0.2.1). GitHub repo: https://github.com/lehkost/dramavis. Kukkonen, K. (2013). Plot. Hühn, P. et al. (eds.): The Living Handbook of Narratology. Hamburg. http://www.lhn.uni-hamburg.de/article/plot.
- Moretti, F. (2011). Network Theory, Plot Analysis. Stanford Literary Lab Pamphlets, No. 2 (1. Mai 2011), http://litlab.stanford.edu/LiteraryLabPamphlet2.pdf.
Bibliography (3/4)
- Park, G.-M., Kim, S.-H., Cho, H.-G. (2013). Structural Analysis on Social Network Constructed from Characters in Literature Texts. Journal of Computers 8.9, pp. 2442–2447, http://ojs.academypublisher.com/index.php/jcp/article/view/jcp080924422447/7672.
- Pfister, M. (1977). Das Drama. Theorie und Analyse. München: Fink.
- Pohl, M., Reitz, F., Birke, P. (2008). As Time Goes by. Integrated Visualization and Analysis of Dynamic Networks. AVI 2008 – Proceedings of the Working Conference on Advanced Visual Interfaces, Neapel, pp. 372–375, http://doi.acm.org/10.1145/1385569.1385636.
- Prado, S. D., Dahmen, S. R., Bazzan, A. L. C., Mac Carron, P., Kenna, R. (2016). Temporal Network Analysis of Literary Texts. Advances in Complex Systems (ACS) 19(3), pp. 1–19, https://arxiv.org/pdf/1602.07275.
- Rochat, Yannick (2014). Character Networks and Centrality. Thèse de Doctorat, Lausanne, https://infoscience.epfl.ch/record/203889/files/yrochat_thesis_infoscience.pdf.
- Stiller, J., Nettle, D., Dunbar, R. I. M. (2003). The Small World of Shakespeare's Plays. Human Nature 14, pp. 397–408, https://www.staff.ncl.ac.uk/daniel.nettle/shakespeare.pdf.
Bibliography (4/4)
- Stiller, J., Hudson, M. (2005). Weak Links and Scene Cliques Within the Small World of Shakespeare. Journal of Cultural and Evolutionary Psychology 3, pp. 57–73.
- Trilcke, P., Fischer, F., Göbel, M., Kampkaspar, D. (2015). 200 Years of Literary Network Data [blogpost], https://dlina.github.io/200-Years-of-Literary-Network-Data/.
- Trilcke, P., Fischer, F., Göbel, M., Kampkaspar, D., Kittel, C. (2016). Theatre Plays as 'Small Worlds'? Network Data on the History and Typology of German Drama, 1730–1930. Digital Humanities 2016. Conference Abstracts. Jagiellonian University & Pedagogical University, Kraków, pp. 385–387, http://dh2016.adho.org/abstracts/407.
- Waumans, M. C., Nicodème, T., Bersini, H. (2015). Topology Analysis of Social Networks Extracted from Literature. Plos One, 3 June 2015, http://dx.doi.org/10.1371/journal.pone.0126470.
- Xanthos, A., Pante, I., Rochat, Y., Grandjean, M. (2016). Visualising the Dynamics of Character Networks. Digital Humanities 2016. Conference Abstracts. Jagiellonian University & Pedagogical University, Kraków, pp. 417–419, http://dh2016.adho.org/abstracts/407.
(Presentation done with reveal.js in Sublime Text 3 on Fedora 26.)
end of presentation 🐬