Course 2021-2022 a.y.


Department of Decision Sciences

Course taught in English
Go to class group/s: 31
CLMG (6 credits - II sem. - OP  |  12 credits SECS-S/01) - M (6 credits - II sem. - OP  |  SECS-S/01) - IM (6 credits - II sem. - OP  |  SECS-S/01) - MM (6 credits - II sem. - OP  |  SECS-S/01) - AFC (6 credits - II sem. - OP  |  SECS-S/01) - CLELI (6 credits - II sem. - OP  |  SECS-S/01) - ACME (6 credits - II sem. - OP  |  SECS-S/01) - DES-ESS (6 credits - II sem. - OP  |  SECS-S/01) - EMIT (6 credits - II sem. - OP  |  SECS-S/01) - GIO (6 credits - II sem. - OP  |  SECS-S/01) - DSBA (6 credits - II sem. - OP  |  SECS-S/01) - PPA (6 credits - II sem. - OP  |  SECS-S/01) - FIN (6 credits - II sem. - OP  |  SECS-S/01)
Course Director:

Classes: 31 (II sem.)

Class-group lessons delivered online

Suggested background knowledge

Given the theoretical nature of the course, the student is supposed to have a good background in basic mathematics and a familiarity with logical/mathematical reasoning. In particular, knowledge of the following tools at an intermediate level is presumed: - Calculus and algebra - Dynamical systems - Game Theory.

Mission & Content Summary


The course presents the main theoretical developments of the modern field of social networks, as these have been applied to understand some of the most important social and economic phenomena that are central to our highly connected societies. The emphasis of the course is on the theory, but we shall also illustrate matters with examples from real-world social networks. Throughout, our main concern is to develop a formal and systematic manner of understanding how social structure (i.e. the pattern of connections) affect social behavior.


  1. Graphs: definitions and measures – basic concepts: degree, distance, component, clustering, betweenness, etc.
  2. Types of networks: lattice, tree/hierarchic, random, etc.
  3. Some real-world examples: a glimpse into its wide diversity.
  4. Forces/mechanisms at work: 
    • Distance/geography.
    • Popularity.
    • Link strength and intermediation.
    • The social environment: homophily and socialization.
    • Positive and negative relationships: structural balance.
  5. Diffusion, learning, and seach in networks:
    • Epidemics: contagion processes in a large social networks.
    • Behavioral dynamics: frequency-dependent diffusion.
    • Learning in social networks: de Groot model.
    • Searching and routing in large social networks.
    • Web-filtering and search in large information networks.
  6. Networks and games:  
    • Traffic and congestion.
    • Matching and markets.
    • Bargaining and power.
    • Public goods.
    • Cooperation.
    • Coordination.

Intended Learning Outcomes (ILO)


At the end of the course student will be able to...
  • Be familiar with the basic tools and concepts of network theory.
  • Be competent in applying game theoretic reasoning to the study of networks and network phenomena.
  • Understand, from a network perspective, important non-strategic phenomena such as search, contagion, diffusion, or learning.
  • Understand, from a network perspective, important strategic phenomenacongestion, matching and trade, power and bargaining, public goods, cooperation, or coordination.


At the end of the course student will be able to...
  • understand the main components (tools and concepts) of the modern field of social networks
  • apply the aforementioned tools and concepts to the study of some of the most important social and economic phenomena
    • epidemic processes, biological and social
    • the diffusion of knowledge and technologies
    • the generation of opinions and beliefs through social interaction
    • search in a large and complex information web
    • the interplay of markets and networks
    • bargaining power in a social network 
  • relate the theoretical developments to the study of real-world instances of the above-listed phenomena

Teaching methods

  • Online lectures
  • Exercises (exercises, database, software etc.)
  • Individual assignments
  • Interactive class activities (role playing, business game, simulation, online forum, instant polls)


Exercises are distributed and solved in practice classes taught by a teaching assistant. All classes, both the main lectures and the preactice classes, will aim at being very interactive.

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  • Oral individual exam
  • Individual assignment (report, exercise, presentation, project work etc.)


The exam is worth 70% of the grade and the problem sets 30%. This applies to both attending and non-attending students.


The written individual exam and the oral exam will allow us to assess whether the student understand the concepts, methodologies, and main results presented in the course. The exams will not expect any memory-based knowledge but a deep understanding of those concepts and results.


The written exam will also incorporate the reralization of exercises that put the aforementioned concepts, methodologies, and results to work in concrete setups.


The same objective as mentioned above for the exam is also the objective of the individual assignments that will be done throughout the course. These assignments will be evaluated as indicated above and will aslo serve as a preparation for the corresponding part of the exam.




Teaching materials


  • The main textbook is: D. EASLEY, J. KLEINBERG, Networks, Crowds, and Markets, Cambridge University Press, 2010. Pre-print copy available at
  • For a more technical coverage of some of the topics, the following two auxiliary bookscan be used:
    • F. VEGA-REDONDO, Complex Social Networks, Cambridge University Press, 2007.
    • M. JACKSON, Social and Economic Networks, Princeton University Press, 2008.
Last change 22/12/2021 10:22