Social and Economic Networks

BAN434 Social and Economic Networks

Spring 2024

Autumn 2024
  • Topics

    Networks are essential in shaping behavior in many environments. For example, economic production and supply chains are organized as networks; new technologies diffuse in the economy through research and development collaboration networks. Social networks pervade our social and economic lives. They are central in transmitting information about job opportunities and are critical to the trade of many goods and services. The presence of networks makes it essential to understand which network structures can emerge and how networks impact behavior.

    Social scientists have used social networks since early in the 20th century. On the other hand, economic networks are a relatively newer research area that emerged in the late 20th century. This thriving field has applications in finance, marketing, macroeconomics, etc., and is a crucial tool for understanding the workings of modern economies. The rise of big data and advanced computational methods has enabled researchers to study economic networks at an unprecedented scale and level of detail, uncovering new insights into the structure and behavior of economies.

    The course introduces networks and applications of social and economic networks. The course aims to provide students with the theoretical foundations of network theory and help them understand behavior and outcomes in networked societies.

    The course contains

    • an overview of social and economic networks, as well as empirical observations about network structure;
    • description of network models and models of network formation;
    • models of how network structures impact behavior: diffusion, learning,  games on networks, and networked markets;
    • practical examples of network applications;
    • methods of network visualization. 

  • Learning outcome


    Students obtain essential knowledge of network analysis applicable to real-world data.    



    After completion of the course, the students

    • Can use basic notation and terminology used in network science;
    • Can visualize, describe and compare networks;
    • Can use main network models and primary models of network formation;   
    • Can analyze processes in networks (analyze how network structures affect networked societies) and understand which network structures are likely to emerge;   
    • Have developed practical skills in network analysis in R programming language;
    • Can analyze real-world networks.



    • Learn new methods of network analysis and apply them to real-world networks.

  • Teaching

    Plenary lectures, possibly guest lectures.

  • Recommended prerequisites

    Basic knowledge of mathematics (standard concepts from calculus, linear algebra, probability, and statistic, which correspond to MET1 Mathematics for economists and MET2 Statistics for economists).

    An introductory programming experience (preferably in R).

  • Required prerequisites


  • Credit reduction due to overlap


  • Compulsory Activity

    One obligatory assignment. This assignment must be accepted in order to get admission to the exam.

  • Assessment

    4-hour written individual home exam. The exam will be in English.

  • Grading Scale

    A - F.

  • Computer tools

    The course will use R, which is open-source. Details regarding the installation of different packages and additional tools will be provided.  

  • Literature

    Compulsory literature:

    i) Matthew O. Jackson (2008) Social and Economic Networks, Princeton University Press.

    ii) Selected notes/overheads available in Canvas.


    Recommended literature:

    There can be recommendations on further reading (not relevant to the exam).


ECTS Credits
Teaching language

Autumn. Will be offered Autumn 2023.

Course responsible

Professor Roman Kozlov, Department of Business and Management Science.