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Online Course

Social and Economic Networks: Models and Analysis

Stanford University via Coursera

6.6k
Found in Economics
  • Provider Coursera
  • Cost Free Online Course (Audit)
  • Session In progress
  • Language English
  • Certificate Paid Certificate Available
  • Effort 3-6 hours a week
  • Duration 8 weeks long
  • Learn more about MOOCs

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Overview

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: http://web.stanford.edu/~jacksonm/Networks-Online-Syllabus.pdf

You can find a short introductory videao here: http://web.stanford.edu/~jacksonm/Intro_Networks.mp4

Syllabus

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

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Reviews for Coursera's Social and Economic Networks: Models and Analysis Based on 11 reviews

  • 5 stars 64%
  • 4 stars 27%
  • 3 star 9%
  • 2 star 0%
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  • 1
Gregory S
4 years ago
by Gregory 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 algebra. You don't need to know anything about social networks ahead of time to take this course, but having basic familiarity with networks will help things go a bit smoother. The course has 7 weeks of lecture content covering network basics, measures of centrality, network formation models and diffusion, learning and games on networks. You'll also …
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Ronny W
2 years ago
by Ronny 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 introducing the basic concepts like centrality measures, degree distributions, diameter, weak and strong ties, ... Then it goes on with several types of random networks and fitting them with real-world networks. Further on it handles strategic network formation, diffusion on networks, learning and games on networks.

The course materi…
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Anonymous
4 years ago
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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