Using Archival Texts to Create Network Graphs of Musicians in Early
Modern Venice

Mollie Ables

mables@indiana.edu

Indiana University, United States of America

Tassie Gniady

ctgniady@iu.edu

Indiana University, United States of America

Kalani Craig

craigkl@indiana.edu

Indiana University, United States of America

Grace Thomas

ghthomas@umail.iu.edu

Indiana University, United States of America

Adam Hochstetter

adamhoch@umail.iu.edu

Indiana University, United States of America

Introduction

This project uses network graphs to depict musicians' careers in late
seventeenth-century Venice. The

current network graph, viewable on the Musicians in Venice web page,
demonstrates relationships between musicians and the institutions that employed
them. The graph is bimodal, with nodes representing musicians (“people” nodes)
and institutions (“place” nodes). Archival texts are incorporated into the
visualization, with transcriptions of records that indicate musicians' activity
included in the node attributes. The entire project is text-based, with data
derived from XML transcriptions of archival records in addition to assigned
metadata. The current website is a proof of concept for a larger project that
would demonstrate ways of displaying text as part of the network graph and
using texts in creating network visualizations.

The current graph focuses on the career of the composer Giovanni Legrenzi.
Legrenzi worked for several prominent Venetian institutions from the early
1670s to his death in 1690 and ultimately was appointed to the most prestigious
musical posts in the city. The musicians he worked with also served multiple
institutions, either simultaneously or in succession, and their relationships
with other musicians, patrons, and administrators often facilitated their
movement between institutions. Legrenzi's Venetian career presents an excellent
case study of these musical connections in late seventeenth-century Venice, and
studying these connections demonstrates how networks of musicians functioned in
this time period. This study also provides a representative sample of how
network visualization effectively demonstrates patterns in musicians' careers
in Venice.

The texts in the network graph include transcribed administrative documents,
mostly unpublished, from the Venetian institutions where Legrenzi was
active between 1670 and 1690. These are primarily payment, hiring, and
termination records, which document the activity of the musicians employed by
or affiliated with these institutions. Several generations of musicologists
have used these documents to identify where different musicians were working,
when they were employed, and in what capacity (Bonta, 1964; Moore, 1981;
Termini, 1981; and Selfridge-Field, 1994). In this sense, this treatment of the
documents is standard to the field, but applies DH methodology and processes to
create visual representations the data. This provides a new perspective on the
sources. For instance, grouping the “person” nodes by centrality results in
multiple sub-groups, and proximity among these nodes demonstrates shared
institutions and implied communities. (Hanneman and Riddle, 2005: Chapter 10)
(Mary Russell Mitford's text-based network analysis of Robert Southey’s Thalaba
the Destroyer provides an excellent example of this. In addition to
demonstrating the behavior of agile, well-connected nodes, the graph displays
modules of nodes as units of analysis (Wasserman and Faust, 1994: 4). 
Methodology

The transcriptions of the original documents were encoded in XML with tags and
attributes determined by the Text Encoding Initiative (TEI). I used the
XML markup to tag the information I wanted to include in my data while
retaining the complete original text. In general, I tagged the name of the
musician, the name of the institution with which they were connected, and the
date and location of the connection.

9307

9308    v    <div type=”entry" xml:id=”LD0_17” rt=”17">

9309    v    <dateline>

«date when*"1677-02-04">Adi 4 Feb[ra]ro 1677 Cong[regazion]e

9311    Gent[il]e«/date>

9312    |    </dateline>

9313    v |    «pxnote place*"margin">Legrenzi</note> Intesi li sentiment! del
Sig[no]r

9314    |    Maestro «name type=”person" ref=''#GioLeg">Legrenzi«/name>; che
accetto di

9315    continuar a far gli Oratorj secondo il solito; fu eletto di nuovo il

9316    Pre[te] D. «name type=“person" ref=”#BarGri">Bartol[ome]o

9317    |    Gritti«/name>; accid passasse I’istesso uffizio col Stg[no]r

9318    |    «name type=”person" ref=”#GioPes”>Giovanni da Resaro«/name>; con

9319    |    q[ue]sto pero; ch’ egli averse solo da ingerirsi nelle funzioni

di «name type="place" ref=”#fava''>Chiesa</name> solam[ertt]e; e fu

9321    |    preso con tutti li voti.</p>

9322    </div>

9323

XML markup of primary document text

I then extracted the information for the network from the XML document using an
XSLT. For this project, the stylesheet extracted the data assembled in
the person index of the XML document and combined it with the entire text that
documented each event. The resultant document was a CSV file of raw data that
had to be separated into sets of nodes and edges to be read by the
visualization platform. The data visualization for this project was generated
using Gephi, which is an open-source platform for exploratory data
analysis. For this project, I generated the network graph in Gephi by importing
two CSV files: one with the nodes and their attributes and ID numbers, and one
with the edges and the source and target data. The online version of the
network graph includes all the text associated with a “person” node in the
network as a node attribute. When the user selects a person node in the online
version, it displays the transcriptions for every document mentioning that
person in the Information Pane. (I exported the data using the SigmaExporter

plugin for Gephi. Version 0.9.0 from Gephi Thirdpar-

ties Plugins. The code is available on Github.)

Displaying all the text associated with a person node presented a challenge as
the XSLT exported each connection between a musician and an institution as
a new line in the CSV. For instance, if a musician was hired by a church in
1674, received a raise in 1678, and retired in 1686, there would be three lines
in the CSV. When imported into Gephi, this would create three connecting edges
between the person node and the place node. This created a misleading network
graph, as the number of documented connections between people and places
depended on the nature and availability of the records. To combine all the
texts associated with the events for each person in the index, I concatenated
the raw data using a PHP script and saved the results as a TSV file that could
be imported into Gephi. As a result, the entire context of the decrees
and decisions surrounding that musician's activity appears in the Information
Pane, which also displays and links to the institutions associated with that
musician. The

PHP script also included HTML formatting to better distinguish the entries from
one another in the visualization. Once my nodes and edges were imported
into Gephi, I used a layout that grouped the nodes by degree of centrality
(Algorithm in Gephi based on Blondel, Guillaume, Lambiotte, and Lefebvre,
2008. Resolution in Gephi based Lambiotte, Delvenne, and Barahona, 2009).

• ©•    Gephi 0.9.1 - O829.gephi

[615-1]

Network graph in Gephi

Next steps

The current graph is the foundation of a more extensive project that uses
musicians and musical activity in Early Modern Venice to benefit scholars
working with TEI in similar for-hire environments. In these environments, hubs
of activity define the relationship between individual practitioners in the
historical equivalent of a sharing economy. In the long-term, I propose an
online data repository and network graph of musicians working in
seventeenth-century Venice that would eventually provide a roadmap for scholars
embedded in the TEI transcription model, but with an interest in an automated
process for applying TEI text analysis to network analysis.

In addition to the bimodal graph that highlights relationships between
musicians and their employing institutions, I also hope to create a unimodal
graph documenting relationships among the musicians. Creating the unimodal
network and a supporting web application will be a challenge as relationships
between individuals can be complicated and dynamic. This will take considerable
work in effectively diagramming entity relationships and building web
applications that go beyond the “out of the box” functionality of the Gephi
export plugin. The website for the Six Degrees of Francis Bacon project
provides an excellent example of a unimodal graph; here the relationships are
de-

fined by circumstances (such as “father to” or “colleague of”) or actions (such
as “met” or “wrote to”).

Linked Jazz, which uses documents as node attributes to demonstrate
relationships and meaningful connections between jazz musicians in an
interactive network graph, is also an excellent example. Both sites feature
custom-designed entity relationships and interactive features in their web
design that I want to emulate in my own network graph.

For the unimodal version, I would expand the data beyond Legrenzi's Venetian
career, increasing the time frame and the number of institutions represented in
the graph, and include different kinds of sources, such as periodicals,
correspondence, and notarial records. Developing relationship typologies is
such a crucial component to the project. This will require creating custom node
metadata based on the information from the primary sources and agile
development as the number and variety documents expand. The result will be a
project that can better serve other scholars of Venetian music and culture.