Social Network Analysis and Network Economics
An interdisciplinary introduction to social and economic network theory and empirics, with hands-on Python/NetworkX sessions designed for students with no prior programming experience.
Term: Second Semester
Course Overview
Our everyday life is shaped by social and economic networks: the information we have access to, how we form opinions, the spread of disease, labour market outcomes, and even friendship formation. This course provides a rigorous yet accessible introduction to network theory, with a strong empirical component using Python and NetworkX. The course is designed by Prof. Nicolas Carayol.
This section is designed for students with no prior programming experience — the first lab session introduces Python from scratch before moving into network analysis.
Part I — Network Theory
- Introduction and motivation
- Random networks and small worlds
- Degree distributions and preferential attachment
- Clustering, community detection, and homophily
- Centrality: Bonacich, Katz, PageRank
Part II — Network Data (Python / NetworkX)
- Introduction to Python and basic network measures
- Directed graphs and visualisation
- Random and scale-free network generation
- Community detection algorithms
- Centrality measures and their interpretation
Prerequisites
No prerequisites required. No prior programming experience assumed — Python is introduced from scratch in the first lab session.
Assessment
| Component | Weight |
|---|---|
| Individual applied data work | 40% |
| Mini project — data treatment | 20% |
| Mini project — reasoning | 20% |
| Mini project — presentation | 20% |
Students may work in pairs for the mini project.
My Role
In 2023, 2024, and 2025 I was teaching assistant, running the practical lab sessions for the main professeur: Pr. Nicolas Carayol. In 2026 I took on the role of main lecturer, delivering the full course independently and designing the practical sessions.
Key References
- Matthew O. Jackson (2019). The Human Network. Pantheon Books. ← most accessible
- Matthew O. Jackson (2008). Social and Economic Networks. Princeton University Press. ← main reference
- David Easley & Jon Kleinberg (2010). Networks, Crowds, and Markets. Cambridge University Press. Free PDF
- Mark E.J. Newman (2010). Networks: An Introduction. Oxford University Press.
Schedule
| Week | Date | Topic | Materials |
|---|---|---|---|
| 1 | Introduction — The Ubiquity of Social Networks Examples of social and economic networks. Basic graph notation. Why networks matter for economics, sociology, and everyday life. | ||
| 2 | Small Worlds and Random Networks The Milgram experiment. Poisson random networks. The small-world phenomenon. | ||
| 3 | Degree Distributions Degree distributions in real-world networks. Preferential attachment and scale-free networks. The configuration model. The friendship paradox. | ||
| 4 | Clustering, Communities and Homophily Clustering coefficients. Community detection. Small worlds à la Watts & Strogatz. Local search à la Kleinberg. Homophily. | ||
| 5 | Centrality and Influence Centrality measures: Bonacich, Katz, Prestige, and PageRank. Economic interpretation and applications. | ||
| 2 | Lab 1 — Introduction to Python and Basic Network Measures Python basics for students new to programming. First steps with NetworkX: importing data, creating graphs, computing basic measures (degree, density, diameter). | ||
| 3 | Lab 2 — Directed Graphs and Network Visualisation Working with directed graphs in NetworkX. Creating meaningful and interpretable network visualisations. | ||
| 4 | Lab 3 — Random Networks, Scale-Free Networks and Degree Distributions Generating Erdős–Rényi and Barabási–Albert networks. Analysing and visualising degree distributions. | ||
| 5 | Lab 4 — Clustering and Community Detection Computing clustering coefficients. Applying community detection algorithms (Louvain, Girvan-Newman) in NetworkX. | ||
| 6 | Lab 5 — Network Centrality Measures Computing and comparing centrality measures in NetworkX. Visualisation and economic interpretation of results. |