Class Introduction to Social Networks

This module provides an in-depth introduction to social network analysis (SNA), emphasizing practical skills and applications in Python using the NetworkX library. Over 30 hours, students will learn to analyse social structures through networks and graph theory, understand the principles behind social networks, and apply these concepts to real-world data.

1. Foundations of Social Network Analysis
2. Graph Theory Essentials
3. Network Metrics and Measures
4. Network Models and Structures
5. Network Dynamics and Evolution
6. Data Collection, Management, and Visualisation
7. Applied Social Network Analysis Project

In the module "Introduction to Social Networks", innovative teaching methods are used to enhance learning. The flipped classroom approach is adopted, where students pre-study the concepts, allowing class time to be devoted to practical exercises, discussions, and projects. Interactive programming sessions via Jupyter Notebooks allow experimentation with Python and NetworkX in an environment of constant feedback. Project-based learning is essential, with students undertaking projects that involve the collection, analysis, and visualisation of real network data, promoting critical thinking and practical application of theoretical knowledge. By incorporating these methods, the aim is to equip students with the technical skills and analytical mindset needed to excel in social network analysis.

Menczer, F., Fortunato, S., & Davis, C. A. (2020). A first course in network science. Cambridge University Press.