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Stanford University

Social and Economic Networks: Models and Analysis

Stanford University via Coursera


Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions.

The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks. Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids. We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences.

You can find a more detailed syllabus here:

You can find a short introductory videao here:


  • Introduction, Empirical Background and Definitions
    • Examples of Social Networks and their Impact, Definitions, Measures and Properties: Degrees, Diameters, Small Worlds, Weak and Strong Ties, Degree Distributions
  • Background, Definitions, and Measures Continued
    • Homophily, Dynamics, Centrality Measures: Degree, Betweenness, Closeness, Eigenvector, and Katz-Bonacich. Erdos and Renyi Random Networks: Thresholds and Phase Transitions
  • Random Networks
    • Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation.
  • Strategic Network Formation
    • Game Theoretic Modeling of Network Formation, The Connections Model, The Conflict between Incentives and Efficiency, Dynamics, Directed Networks, Hybrid Models of Choice and Chance.
  • Diffusion on Networks
    • Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data.
  • Learning on Networks
    • Bayesian Learning on Networks, The DeGroot Model of Learning on a Network, Convergence of Beliefs, The Wisdom of Crowds, How Influence depends on Network Position..
  • Games on Networks
    • Network Games, Peer Influences: Strategic Complements and Substitutes, the Relation between Network Structure and Behavior, A Linear Quadratic Game, Repeated Interactions and Network Structures.
  • Final Exam
    • The description goes here

Taught by

Matthew O. Jackson


4.5 rating, based on 11 Class Central reviews

4.8 rating at Coursera based on 710 ratings

Start your review of Social and Economic Networks: Models and Analysis

  • Gregory J Hamel ( Life Is Study) completed this course and found the course difficulty to be medium.

    Social and Economic Networks: Models and Analysis is an introductory network theory and analysis course offered by Stanford through the Coursera MOOC platform geared toward learners who are comfortable with basic statistics, probability and linear a…
  • Ronny De Winter completed this course, spending 4 hours a week on it and found the course difficulty to be hard.

    A solid, well-structured, introduction into the theory of network analysis applied to social and economic networks, by Professor Matthew O. Jackson, supported by his book "Social and Economic Networks" (optional). The course starts with introduci…
  • Anonymous

    Anonymous completed this course.

    Excellent course, by one of the finest minds in the field. Lectures were clear, informative, interesting. Assignments were very fair and instructive.
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