Participants are invited to take part in one or two of three half-day-workshops during the first day of the conference:

  1. Introduction to Social Network Analysis (Matthias Bixler, Independent Researcher)
  2. Exponential Random Graph Models for Historical Networks with R (Antonio Fiscarelli, University of Luxemburg)
  3. Analysis of Two-Mode Networks with Python (Demival Vasques Filho, Leibniz Institute of European History Mainz)

The workshop programme is supported by the Digital History & Hermeneutics Doctoral Training Unit (DTU) of the C2DH, funded by the Luxembourg National Research Fund (FNR), and will be free of charge. Participants will be able to register for the workshops when registration for the conference opens.

1. Introduction to Social Network Analysis

Instructor: Matthias Bixler, Independent Researcher

The workshop provides a hands-on introduction to social network analysis for historians and other humanists with little or no prior experience in quantitative methods or SNA. It will consist of three parts. The first part will give a theoretical overview over social network analysis and how it has been adapted to historical research. The second part will cover how to gather social network data and prepare them for analysis. In the third part we will follow a hands-on approach to visualizing and analyzing social network data. While the first part is more theoretical in nature, the second and third parts will contain lab exercises where participants will be able to practice with example data.

In particular, the following topics will be covered:

  • data collection from historical sources
  • structure and management of social network data
  • import and export of data to/from Visone
  • visualizing social network data
  • computing and interpreting important network descriptives

Tools and Skills

We will use Visone to visualize and analyze network data. Visone is a free software package for social network analysis ( with an intuitive graphical user interface. Experience in data analysis or programming is not required.

Participants should bring their own laptop with the latest version of Visone installed. Example data and workshop slides will be made available over the conference website shortly before the workshop.

2. Exponential Random Graph Models for Historical Networks with R

Antonio Fiscarelli, University of Luxemburg

This tutorial will provide an introduction to Exponential Random Graph Models followed by some example applications on historical networks.

Exponential Random Graph Models (ERGMs) are a family of statistical models that help discover and understand the processes underlying network formation [1–3]. They have been used extensively in social network analysis and are popular in various fields such as sociology [4, 5], archaeology [6], and history [7]. ERGMs provide a model for a network that includes covariates – variables that relate to two or more nodes – which cannot be addressed using traditional methods. They can represent features such as:

  • homophily: the tendency of similar nodes to form relationships.
  • mutuality: the tendency of node B to form a relationship with node A, if node A is connected to node B.
  • triadic closure: the tendency of node C to form a relationship with node A, if node A is connected to node B and node B is connected to node C.

ERGMs also provide maximum-likelihood estimates for the parameters gov-erning these effects, as well as a goodness-of-fit test for the model. Furthermore, it can simulate networks that match the probability distributions estimated bythe model.

Tools and Skills

The following tools are required:

A basic knowledge of the programming language R is required. For an introduction to R, you can use this tutorial: An understanding of basic concepts of network analysis and its terminology is required as well.


  1. Anderson, C.J., Wasserman, S., Crouch, B.: A p* primer: Logit models for socialnetworks. Social networks 21(1) (1999) 37–662.
  2. Robins, G., Pattison, P., Kalish, Y., Lusher, D.: An introduction to exponential random graph (p*) models for social networks. Social networks 29(2) (2007) 173–1913.
  3. Robins, G., Snijders, T., Wang, P., Handcock, M., Pattison, P.: Recent developments in exponential random graph (p*) models for social networks. Social networks 29(2) (2007) 192–2154.
  4. Goodreau, S.M., Kitts, J.A., Morris, M.: Birds of a feather, or friend of a friend? Using exponential random graph models to investigate adolescent social networks. Demography 46(1) (2009) 103–1255.
  5. Grund, T.U., Densley, J.A.: Ethnic homophily and triad closure: Mapping internal gang structure using exponential random graph models. Journal of Contemporary Criminal Justice 31(3) (2015) 354–3706.
  6. Brughmans, T., Keay, S., Earl, G.: Introducing exponential random graph models for visibility networks. Journal of Archaeological Science 49 (2014) 442–4547.
  7. Breure, A.S., Heiberger, R.H.: Reconstructing science networks from the past. Journal of Historical Network Research 3(1) (2019) 92–117.

3. Analysis of Two-Mode Networks with Python

Demival Vasques Filho (twitter), Leibniz Institute of European History Mainz

Many systems represented by networks are one-mode projections of more complicated structures. Often, the original network has a bipartite architecture comprised of two differenttypes of nodes. Examples are relations based on membership, affiliation, collaboration, employment, ownership, and others.

We use projections of two-mode networks mainly for two reasons. First, we are often more interested in only one of the types of nodes, those representing agency. Second, because there is a myriad of metrics for studying one-mode networks, while there exist fewer well-established metrics to characterize the properties of a two-mode network.

In this workshop, we will learn about the metrics that we employ to directly analyze two-mode networks as, for instance, the degree distribution of both sets of nodes, redundancy, clustering coefficients, and the presence of motifs (small-cycles). Also, we will discuss projections, and how and when to use the different methods for creating one-mode projected networks, such as simple, multi, and weighted graph projections.

Tools and Skills

We highly suggest that all attendees have the Anaconda Distribution already installed ontheir computers. Anaconda provides Python, Jupyter Notebook, and all the Python libraries that we will use during the workshop.

Participants do not need to have previous knowledge of two-mode networks or Python. Nevertheless, it is helpful to have some familiarity with networks in general, and with the programming language, before attending the workshop.