Course description

Economists use concepts and methods from network science to understand a complex, global, and interconnected world. In network thinking, the fundamental unit of analysis consists of the relationships among interacting units rather than the individual characteristics of these units. The ability to analyze the particular way relationships are organized - i.e. the network structure - is crucial in understanding complex phenomena in nature and society. Network thinking and models are also the very essence of transformative technologies such as AI & blockchain. In this course, we discuss how a network perspective can help us to re-think key issues in economics. More specifically, this course will introduce concepts and methods to map and measure relationships and flows between people, firms, cities, economic sectors, communities, or any other element of a complex economic system. We will discuss how the structure of a system influences its overall performance, why the relative position in a network conditions the access to critical resources, and how relationships are created and dissolved over time. This course consists of lectures combined with computer exercises and online tutorials.

What you will learn

  1. What a network-based paradigm is, and how it can be applied to economics
  2. How to identify, describe, and analyze the structure of networks
  3. How the structure of economic systems influence their performance
  4. How networks evolve in space and over time
  5. Basic programming skills (in R) and advanced network analysis techniques

Meet the instructors

Pierre-Alexandre Balland -
Matté Hartog -


The overall grade for the class will be based on an individual (mid-term) exam (50%) and a report of a collective research project (50%).

Research project

The report of the research project involves the collection, examination, and analysis of economic network data followed by a short essay reviewing empirical and theoretical arguments. Groups of 2-3 students will focus on a specific economic question and use network thinking and network analysis tools to answer it. Make sure not to apply blindly the tools you will learn in the class but rather tell a story with your network data. You will be guided along your projects in group meetings. The outcome of your project should be (1) a slidedeck (10 slides max), (2) a 2000-word long paper (excluding references) and (3) the R scripts you used to produce the network graphs and compute the network metrics. This material should be submitted to this Dropbox folder by November 5 at the latest (week 44 is free so you can finalize the project).

Group meetings

One of the most important parts of this class is your network project. You will learn many new skills, that can lead to more advanced research or even a business opportunity. But this project is also challenging, from truly applying network thinking and collecting the data, to presenting your finding in a clear and structured way. These group meetings are milestones to make sure that you stay on schedule and do not get lost in the project complexity. Please submit your work in progress 48h before your weekly group meetings to this folder.


There is no class reader. The weekly readings are provided on this web-page and slides/videos will be regularly uploaded here. All articles listed should be considered mandatory reading. Additional online materials might be assigned throughout the quarter.

Course Schedule

Week Day Date Time Location Activity Lecturer
36 Thursday 07/09 15:15-17:00 MIN - 2.02 Lecture 1 Balland
36 - - - Online (video) Lab 1 Balland
36 Thursday 07/09 17:15-19:00 MIN - 2.02 Lecture 2 Balland
36 - - - Online (video) Lab 2 Balland
37 Thursday 14/09 15:15-17:00 MIN - 2.01 Lecture 3 Balland
37 - - - Online (video) Lab 3 Balland
38 - - - - Ideation time -
39 Thursday 28/09 15:15-17:00 MIN - 2.01 Lecture 4 Balland
39 - - - Online (video) Lab 4 Balland
40 Thursday 05/10 15:15-19:00 Online Meeting Hartog
41 Thursday 12/10 13:30-14:30 EDUC-GAMMA Exam Balland
42 Thursday 19/10 15:15-19:00 Online Meeting Hartog
43 Thursday 26/10 15:15-19:00 Online Meeting Hartog
44 - - - - Project -
Lecture 1: Introduction to network science SLIDES

Topics covered
- Overview of class
- Introduction to network thinking
- Networks in natural sciences, social sciences, and business
- Economics and networks

- Barabasi, A. L. (2012) The network takeover, Nature Physics 8 (1), 14-16 PDF
- Ter Wal, A. L., and Boschma, R. A. (2009) Applying social network analysis in economic geography: framing some key analytic issues. The Annals of Regional Science 43 (3): 739-756 PDF
- Hanneman, R.A. and Riddle, M. (2005) Introduction to social network methods. Riverside, CA: University of California, Riverside - Chapter 1 PDF
- An application of ONA by Deloitte PDF

Lab 1: Network Analysis in R - Introduction to R SLIDES

Topics covered
- Network data collection (research projects)
- Software for network analysis
- Introduction to R and RStudio
- Basic programming skills


Lecture 2: Graph theory and Complex networks SLIDES

Topics covered
- Graphs and matrices
- Key concepts: nodes, links, structure
- Random networks
- Small worlds
- Growing networks
- Key structural patterns of real-world networks
- Principles to keep in mind when working with your own network data
- Scope and milestones of the project

- Barabasi, A. L. (2016) Network Science. Cambridge, England: Cambridge University Press - Chapter 2 PDF
- Hanneman, R.A. and Riddle, M. (2005) Introduction to social network methods. Riverside, CA: University of California, Riverside - Chapters 2 PDF, 3 PDF, 5 PDF & 7 PDF
- Barabasi, A. L., and Albert, R. (1999) Emergence of scaling in random networks, Science 286 (5439): 509-512 PDF
- Watts, D. J., and Strogatz, S. H. (1998) Collective dynamics of ‘small-world’ networks, Nature 393 (6684): 440-442 PDF

Lab. 2: Network analysis in R - Network Data

Topics covered
- Basic matrix algebra VIDEO 2.1
- Network data management VIDEO 2.2
- Creating an igraph object (from raw data) VIDEO 2.3

- Balland, P.A. (2017) Economic Geography in R: Introduction to the EconGeo Package, Papers in Evolutionary Economic Geography, 17 (09): 1-75 PDF
- Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006 PDF

Lecture 3: Centrality and power SLIDES

Topics covered
- Why positions of actors/nodes in a network matter
- Degree, betweenness, and closeness centrality
- Strength of weak ties, structural holes, and network closure

- Granovetter, M. (1985) Economic action and social structure: The problem of embeddedness, American journal of sociology 91 (3): 481-510 PDF
- Burt, R. S. (2004) Structural holes and good ideas. American journal of sociology 110 (2): 349-399 PDF
- Hanneman, R.A. and Riddle, M. (2005) Introduction to social network methods. Riverside, CA: University of California, Riverside - Chapters 9 PDF & 10 PDF

Lab. 3: Network analysis in R - Computing global & local indicators

Topics covered
- Structural analysis of global networks VIDEO 3.1
- Computing different forms of centrality VIDEO 3.2
- Mastering the igraph R package

Lecture 4: The economy as a complex system SLIDES

Topics covered
- Endogenous mechanisms of growth in economic systems
- Mapping economic systems as 2-mode networks
- Relatedness and evolution
- Predicting changes in economic systems
- An application to European innovation policy

- Hidalgo, C., Balland, P.A., Boschma, R., Delgado, M., Feldman, M., Frenken, K., Glaeser, E., He, C., Kogler, D., Morrison, A., Neffke, F., Rigby, D., Stern, S., Zheng, S., and Zhu, S. (2018) The Principle of Relatedness, Proceedings of the 20th International Conference on Complex Systems, forthcoming [PDF]
- Hidalgo, C. A., Klinger, B., Barabasi, A. L., & Hausmann, R. (2007). The product space conditions the development of nations. Science, 317(5837), 482-487 PDF
- Balland, P.A., Boschma, R., Crespo, J. and Rigby, D. (2019) Smart Specialization policy in the EU: Relatedness, Knowledge Complexity and Regional Diversification, Regional Studies, forthcoming PDF
- INET Webinar How Regions Can Re-invent Themselves by Pierre-Alexandre Balland

Lab. 4: Mapping the structure of economic systems in R

Topics covered
- Computing relatedness between (economic) activities VIDEO 4.1
- Relatedness density and predicting entry (diversification) VIDEO 4.2
- Visualization of complex networks VIDEO 4.3

Additional reading
Students are not required to purchase any books to follow this course. If you are interested in additional reading, these three books make an excellent introduction to the world of network science and network analysis:

  • Barabasi, A.L. (2002) Linked: The New Science of Networks. Cambridge, MA: Perseus.
  • Newman, M.E.J. (2010) Networks: An Introduction. Oxford, England: Oxford University Press.
  • Wasserman, S., and Faust, K. (1994) Social Network Analysis: Methods and Applications. Cambridge, England: Cambridge University Press.

Useful websites