This course provides an introduction to network analysis, specifically designed for humanities students. Network analysis offers powerful methods for examining relationships, connections, and structures within cultural, historical, and literary contexts. The course covers both the theoretical foundations of network science and the practical application of network analysis and visualisation tools in humanities research.
Students will explore key concepts and methodologies while gaining hands-on experience in mapping social networks in historical archives, analysing character relationships in literature, and tracing intellectual networks in scholarly communication. No prior programming experience is required, as the course provides a beginner-friendly introduction to computational methods.
The course consists of eight sessions, each including a lecture, a seminar, and a hands-on session. Lectures introduce fundamental concepts and methods, while seminars encourage critical engagement with research projects applying network analysis to different types of humanities data. Hands-on sessions provide practical training with tools, such as Python libraries for network analysis and Gephi.
By the end of the course, students will be able to critically evaluate network-based approaches, apply network analysis techniques to their research, and interpret visualisations and metrics in meaningful ways.
Academic Year: 2024/25
Time: Thursdays, 13:00-16:00
Place: 65 Rue de Richelieu, 75002 Paris
Teacher: Dr Katarzyna Anna Kapitan
- Attendance and active participation – 20%
- Article-review (in-class presentation & written submission) – 30%
- Final project (in-class project presentation & digital asset submission) – 50%
Note: In order to pass the class you need to pass all three grading components (Attendance, Article-review, and Final Project).
The deadline for the final project submission is 11 May 2025, see the submission Guidelines (https://github.com/KAKDH/ENC_HN_NA/blob/main/slides/Kapitan_2025_NA_Final_Assignement_Guidelines.md).