subject
Intro

Coursera: Social and Economic Networks: Models and Analysis

 with  Matthew O. Jackson
Class Central Course Rank
#3 in Subjects > Social Sciences > Economics

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

10 Student
reviews
Cost Free Online Course (Audit)
Pace Upcoming
Subject Economics
Institution Stanford University
Provider Coursera
Language English
Certificates Paid Certificate Available
Hours 3-6 hours a week
Calendar 8 weeks long
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FAQ View All
What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

10 reviews for Coursera's Social and Economic Networks: Models and Analysis

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2 out of 2 people found the following review useful
2 years ago
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 algebra. You don't need to know anything about social networks ahead of Read More
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.
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1 out of 1 people found the following review useful
a year ago
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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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7 months ago
Susy Cabrales completed this course.
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