A System of Trope Elements
Using Network Models to Understand Interrelations Within the Transmission of Trope Complexes
How do individual tropes (short musical sequences) spread geographically, and how can different manuscripts be grouped based on shared content? Previous approaches have manually computed troper similarities based on concordant trope elements and used them for clustering from which the groupings can be read in detail. While the time of creation of surviving medieval manuscripts is not necessarily identical to the time of origin of the chants they contain, the geospatial distribution of different chants within all extant manuscripts of various provenances offers clues about potential historical layers of their origin.
Interactive Figure in Poster Communities in Medieval Troper Networks are Shaped by Carolingian Politics (DLfM 2023)
Network of Tropes with Territories after Treatise of VerdunData
The data contains entries from volumes I (Christmas) and III (Easter) of the Corpus Troporum (CT) project, representing a central part of the cycle of proper tropes. Other volumes are in preparation. It was transcribed semi-automatically and contains as of today:- Trope elements from 100 tropers (manuscripts)
- Tropes for 49 different primary chants
- Number of unique trope elements: 1 413
Older Visualisations (MedRen 2023)
- Reproduction of Hileys Network
- Troper Network with Jaccard Metric
- Troper Network plotted on Map
- Troper Network plotted with Trope Elements on Map
Future Tasks
At present, common descriptive community detection algorithms are being used. However, the aim of the study is to understand the underlying generative structures, which is not possible with descriptive community detection.1 The plan is to develop a musicologically informed generative model for the transmission of trope chants that can describe the data more effectively than a baseline model (Hierarchical Stochastic Block Model, see the Figure below).
Inferred Hierarchical Network Structure of Manuscripts produced by graph-tools
1 Peixoto, Tiago P. Descriptive vs. inferential community detection in networks: Pitfalls, myths and half-truths. Cambridge University Press, 2023.