This hands-on workshop provides a critical introduction to Social Network Analysis (SNA) tailored for researchers in the arts, humanities, and social sciences. It may be especially relevant for those who are working with large historical datasets, or keen to do so in the future.

Moving beyond the visualisation of simple “ego networks”, this session balances practical approaches to SNA with critical data theory. Using a real-world dataset of plant collectors drawn from nineteenth-century botanical specimen labels, participants will learn to clean and transform raw data into relational node and edge lists with RStudio. We will then import this structured data into Gephi to build, filter, style, and analyse dynamic temporal networks. By the end of the session, researchers will gain both a computational pipeline to process their own historical data and the theoretical framework required to interpret complex network metrics and visualisations responsibly.

Prior to the session, participants must complete a 2-hour self-guided Moodle module to help familiarise themselves with key social network analysis concepts, as well as setting up RStudio and Gephi on their laptop. This helps to ensure we can spend more time on analysis and discussion in the workshop itself.

Key Learning Objectives:

• Evaluate qualitative research questions and historical sources through a network analysis framework. Calculate and critically interpret key network centrality metrics (degree, betweenness, eigenvector) and community detection algorithms (modularity).
• Navigate real-world data challenges such as missing data, source bias, entity disambiguation, and standardisation.
• Transform raw (meta)data into structured edge/node tables using pre-prepared R scripts in RStudio.
• Experiment with dynamic network visualisation, layout algorithms, and filtering in Gephi.

Participants will need to bring their own laptop and R, R Studio, and Gephi can be downloaded for free prior to the workshop.