The workshop uses a lesson from The Programming Historian to introduce humanities researchers to historical network analysis.
In many humanities disciplines, researchers will come across networks: personal networks, correspondence networks, literary networks, to name just a few. But analysing these networks and drawing conclusions from the data and visualisations doesn't come naturally and following an online-tutorial on your own often turns into a frustrating exercise in googling jargon and trying to make sense of code and data that is unintelligible.
In this workshop, we will cover how to use the NetworkX package for working with network data in Python and how to analyse humanities network data. We will look into network structure and path lengths, important or central nodes, and communities and subgroups.
We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.
We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.
- Annika Rockenberger
- Anne Claire Fouilloux
- Ana Costa Conrado
A list of contributors to the lesson can be found in AUTHORS.
This lesson is based on the work of John Ladd, Jessica Otis, Christopher N. Warren, and Scott Weingart. Their lesson "Exploring and Analyzing Network Data with Python" has been published under the Creative Commons Attribution 4.0 License CC-BY 4.0 on The Programming Historian on August 23, 2017. It was peer-reviewed by Elisa Beshero-Bondar, Anne Chao, and Qiwei Li and edited by Brandon Walsh. The last modification has been made on May 19, 2018. It is on this version the lesson here is based on.
John Ladd, Jessica Otis, Christopher N. Warren, and Scott Weingart, "Exploring and Analyzing Network Data with Python," The Programming Historian 6 (2017), https://programminghistorian.org/en/lessons/exploring-and-analyzing-network-data-with-python. Adapted by Annika Rockenberger, Anne Fouilloux, and Ana Costa Conrado. 2019.