Social and Economic Networks: Models and Analysis

Stanford University via Coursera

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

30 hours
Sessions

On-Demand
Level

Advanced
Subtitles

Arabic, French, Portuguese, Italian, German, Russian, English, Spanish, Kazakh, Hindi, Swedish, Korean, Greek, Chinese, Thai, Ukrainian, Japanese, Indonesian, Polish, Dutch, Turkish, Hungarian, Bengali, Pashto, Urdu, Azerbaijani, Farsi

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Overview

Class Central Tips

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

Reviews

4.5 rating, based on 11 Class Central reviews

4.8 rating at Coursera based on 741 ratings

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

Gregory J Hamel ( Life Is Study) @greg

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…

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 be introduced to Gephi, a software tool for network visualization and analysis. The 8th week is reserved for a final exam.

Social and economic networks provides all the raw information you need to get a solid grounding in network theory and analysis, but the presentation style is impersonal so the content is not particularly engaging. The professor is knowledgeable and appears on screen while explaining lecture slides, but he shows little emotion. While the lectures can get a bit intimidating with equation after equation, the homework exercises and final exam are easier than the lectures might suggest. You get 2 attempts on each chapter quiz and 1 attempt on the final; a score of 70% or more is required for a certificate and 90% or more will earn you a certificate with distinction.

All in all, social and economic networks is worthwhile course if you are interested in social networks and aren't intimidated by a bit of math, but I wouldn't take it for fun. If you want to take a course on the same subject that is lighter on math, consider Coursera's Networked Life from UPenn. It covers similar topics in a manner that is a bit more accessible to the average person.

I give this course 3.5 out of 5 stars: Good.
Ronny De Winter @RonnyDeWinter

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…

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 material has lots of references to research papers. For the very motivated students, there are optional empirical problem sets and advanced problem sets.

Every session ends with a problem set to test your understanding of the material.
The final exam is rather short and tests on the main concepts of the course.

This is a graduate-level university semester course from Stanford. It takes time and discipline to complete. A good understanding of basic stats, probability and linear algebra is needed to get through the course. Otherwise, some of the material may appear to be daunting.

This course is a must-do for everyone with a strong interest in the subject. If you just want something to learn for fun there might be better alternatives. The course is somewhat dry and mathematical. It could be made a bit more engaging.
Anonymous

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