Netzwerkdynamik, Plotanalyse – Zur Visualisierung und Berechnung der 'progressiven Strukturierung' literarischer Texte
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).
Conference abstracts for DHd2017, Bern. (http://www.dhd2017.ch/)
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.
Our paper offers a critical examination of the concept and practice of Distant Reading, as coined by Franco Moretti in 2000. We consider several definitions of the term and look for possible operationalizations. It becomes clear that Distant Reading has largely been a theoretical vehicle or mere buzzword in the past one and a half decades that adapts only slowly to the practices and technological standards of the Digital Humanities. In the light of these findings, we conclude with an examination of the operational potential of Foucauldian discourse analysis.