Life(!) on Stage: Building an interface for the network analysis of TEI-encoded drama corpora
A “Network Analysis” section was arranged at the XVIIIth Interna- tional Academic Conference on Economic and Social Development at the Higher School of Economics on 11–12 April 2017. For the third year, this section invited scholars from sociology, political science, management, mathematics, and linguistics who use network analysis in their research projects. During the sessions, speakers discussed the development of mathematical models used in network analysis, studies of collaboration and communication networks, networks’ in- uence on individual attributes, identifcation of latent relationships and regularities, and application of network analysis for the study of concept networks.
The speakers in this section were E. V. Artyukhova (HSE), G. V. Gra- doselskaya (HSE), M. Е. Erofeeva (HSE), D. G. Zaitsev (HSE), S. A. Isaev (Adidas), V. A. Kalyagin (HSE), I. A. Karpov (HSE), A. P. Koldanov (HSE), I. I. Kuznetsov (HSE), S. V. Makrushin (Fi- nancial University), V. D. Matveenko (HSE), A. A. Milekhina (HSE), S. P. Moiseev (HSE), Y. V. Priestley (HSE), A. V. Semenov (HSE), I. B. Smirnov (HSE), D. A. Kharkina (HSE, St. Petersburg), C. F. Fey (Aalto University School of Business), and F. López-Iturriaga (Uni- versity of Valladolid).
This essay questions whether digital literary studies can still be meaningfully regarded as part of literary studies. This heretical question is motivated by a praxeological view of a research project for the network analysis of dramatic texts, in particular by reflecting on the project’s underlying ›epistemic thing‹, which in this case consists of specifically-formatted structural data (and not the actual primary texts themselves). What does this corpus of structural data, which was extracted from 465 plays spanning the period from 1730 to 1930, have to do with the ›epistemic things‹ of literary studies? We explore this question by providing insight into our analyses, which describe the structural evolution of the ›plays‹, try to locate ›small world‹ properties in our corpus, and develop new metrics for plot analysis. The results show not only how digital methods can supplement or enrich literary studies; they also raise questions about how digital the field of literary studies already is, since its research objects are increasingly available in digital forms.
Of late, the network analysis of literary texts has grown into an independent research field of digital literary studies. Since analysing the network structure of unique texts promises just marginal results, the perspective should shift towards a ‘distant reading’ of hundreds or thousands of texts. In this paper, we describe how the process of formalising literary data is facilitated by machine-readable corpora comprising hundreds of dramatic texts in several languages. Taking a corpus of roughly 500 German-language dramas as an example, we demonstrate how the calculation of network metrics and visualisations can deliver new material for interpretation and offer new insights into the evolution of drama.
Das Projekt ‘Digitale Netzwerkanalyse dramatischer Texte’ steht in der Tradition strukturanalytischer Ans¨atze in der Literaturwissenschaft (allgemein Titzmann 1977), die es einerseits im Sinne eines konsequent netzwerkanalytischen Relationismus (mit Rekurs auf die Social Network Analysis, siehe u. a. Wasserman/Faust 1998), andererseits unterstutzt durch Verfahren der automatisierten ¨ Datenerhebung und -auswertung weiterentwickelt, um sie auf gr¨oßere Textkorpora anzuwenden und so umfassende relationale Daten uber Prozesse des literaturgeschichtlichen Strukturwandels ¨ gewinnen zu k¨onnen.
The problem of link prediction gathered a lot of attention in the last few years, arising in dierent applications ranging from recommendation systems to social networks. In this paper, we will describe the most popular similarity indices, compare their performance in their ability to show links with the highest probability of being removed from initial network and describe the approach that allows to use them to predict missing links using supervised machine learning. We will show the accuracy of prediction of this method on examples of real networks.
Contributions in this volume focus on computationally efficient algorithms and rigorous mathematical theories for analyzing large-scale networks. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks.
This proceeding is a result of the 7th International Conference in Network Analysis, held at the Higher School of Economics, Nizhny Novgorod in June 2017. The conference brought together scientists, engineers, and researchers from academia, industry, and government.
In this paper we introduce RusDraCor — an open corpus of Russian drama for digital literary & linguistic research. The corpus (rus.dracor.org) contains plays from the middle of XVIII to the first third of XX century provided with structural (plus some semantic) markup and metadata. Texts are encoded in the XML-based standard TEI, widely used in building corpora for the humanities. We describe the contents and annotation layers of our corpus, provide some details on its development and enrichment, and finally describe three research cases. Each case demonstrates the use of RusDraCor to answer specific questions about composition, structural features and historical evolution of Russian drama.