Social and Economic Networks: Models and Analysis

Name: Social and Economic Networks: Models and Analysis
Rating: 4.8023715415019765 (759 reviews)

Instructor: Matthew O. Jackson

74,730 already enrolled

8 modules

Gain insight into a topic and learn the fundamentals.

759 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

8 modules

Gain insight into a topic and learn the fundamentals.

759 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

20 assignments

Taught in English

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There are 8 modules in this course

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

Examples of Social Networks and their Impact, Definitions, Measures and Properties: Degrees, Diameters, Small Worlds, Weak and Strong Ties, Degree Distributions

What's included

12 videos3 readings3 assignments

12 videos Total 118 minutes

An Introduction to the Course 4 minutes
1.1: Introduction 9 minutes
1.2: Examples and Challenges 15 minutes
1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23) 8 minutes
1.3: Definitions and Notation 14 minutes
1.4: Diameter 17 minutes
1.5: Diameter and Trees 6 minutes
1.6: Diameters of Random Graphs (Optional/Advanced 11:12) 11 minutes
1.7: Diameters in the World 7 minutes
1.8: Degree Distributions 13 minutes
1.9: Clustering 9 minutes
1.10: Week 1 Wrap 3 minutes

3 readings Total 30 minutes

Syllabus 10 minutes
Slides from Lecture 1, with References 10 minutes
OPTIONAL - Advanced Problem Set 1 10 minutes

3 assignments Total 90 minutes

Problem Set 1 30 minutes
Quiz Week 1 30 minutes
Optional: Empirical Analysis of Network Data using Gephi or Pajek 30 minutes

Homophily, Dynamics, Centrality Measures: Degree, Betweenness, Closeness, Eigenvector, and Katz-Bonacich. Erdos and Renyi Random Networks: Thresholds and Phase Transitions

What's included

11 videos3 readings3 assignments

11 videos Total 105 minutes

2.1: Homophily 10 minutes
2.2: Dynamics and Tie Strength 7 minutes
2.3: Centrality Measures 15 minutes
2.4: Centrality – Eigenvector Measures 13 minutes
2.5a: Application - Centrality Measures 13 minutes
2.5b: Application – Diffusion Centrality 7 minutes
2.6: Random Networks 10 minutes
2.7: Random Networks - Thresholds and Phase Transitions 7 minutes
2.8: A Threshold Theorem (optional/advanced 13:00) 13 minutes
2.9: A Small World Model 7 minutes
2.10 Week 2 Wrap 4 minutes

3 readings Total 30 minutes

Slides from Lecture 2, with references 10 minutes
OPTIONAL - Advanced Problem Set 2 10 minutes
OPTIONAL - Solutions to Advanced PS 1 10 minutes

3 assignments Total 90 minutes

Problem Set 2 30 minutes
Quiz Week 2 30 minutes
Optional: Empirical Analysis of Network Data 30 minutes

Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation.

What's included

12 videos3 readings4 assignments

12 videos Total 143 minutes

3.1: Growing Random Networks 18 minutes
3.2: Mean Field Approximations 8 minutes
3.3: Preferential Attachment 11 minutes
3.4: Hybrid Models 15 minutes
3.5: Fitting Hybrid Models 17 minutes
3.6: Block Models 10 minutes
3.7: ERGMs 9 minutes
3.8: Estimating ERGMs 16 minutes
3.9: SERGMs 10 minutes
3.10: SUGMs 6 minutes
3.11: Estimating SUGMs (Optional/Advanced 21:03) 21 minutes
3.12: Week 3 Wrap 3 minutes

3 readings Total 30 minutes

Slides from Lecture 3, with references 10 minutes
OPTIONAL - Advanced Problem Set 3 10 minutes
OPTIONAL - Solutions to Advanced PS 2 10 minutes

4 assignments Total 120 minutes

Problem Set 3 30 minutes
Quiz Week 3 30 minutes
Optional: Empirical Analysis of Network Data 30 minutes
Optional: Using Statnet in R to Estimate an ERGM 30 minutes

Game Theoretic Modeling of Network Formation, The Connections Model, The Conflict between Incentives and Efficiency, Dynamics, Directed Networks, Hybrid Models of Choice and Chance.

What's included

15 videos3 readings2 assignments

15 videos Total 209 minutes

4.1: Strategic Network Formation 12 minutes
4.2: Pairwise Stability and Efficiency 15 minutes
4.3: Connections Model 11 minutes
4.4: Efficiency in the Connections Model (Optional/Advanced 12:41) 13 minutes
4.5: Pairwise Stability in the Connections Model 7 minutes
4.6: Externalities and the Coauthor Model 11 minutes
4.7: Network Formation and Transfers 17 minutes
4.8: Heterogeneity in Strategic Models 14 minutes
4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47) 14 minutes
4.10: Pairwise Nash Stability (Optional/Advanced 11:34) 12 minutes
4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57) 12 minutes
4.12: Evolution and Stochastics (Optinoal/Advanced 16:05) 16 minutes
4.13: Directed Network Formation (Optional/Advanced 16:38) 17 minutes
4.14: Application Structural Model (Optional/Advanced 35:06) 35 minutes
4.15: Week 4 Wrap 4 minutes

3 readings Total 30 minutes

Slides from Lecture 4, with references 10 minutes
OPTIONAL - Advanced Problem Set 4 10 minutes
OPTIONAL - Solutions to Advanced PS 3 10 minutes

2 assignments Total 60 minutes

Problem Set 4 30 minutes
Quiz Week 4 30 minutes

Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data.

What's included

12 videos3 readings3 assignments

12 videos Total 158 minutes

5.1: Diffusion 13 minutes
5.2: Bass Model 12 minutes
5.3: Diffusion on Random Networks 9 minutes
5.4: Giant Component Poisson Case 15 minutes
5.5: SIS Model 18 minutes
5.6: Solving the SIS Model 9 minutes
5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16) 24 minutes
5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47) 23 minutes
5.8b: Application: Financial Contagions (Optional/Advanced 12:47) 13 minutes
5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41) 14 minutes
5.9: Diffusion Summary 4 minutes
5.10: Week 5 Wrap 4 minutes

3 readings Total 30 minutes

OPTIONAL - Advanced Problem Set 5 10 minutes
OPTIONAL - Solutions to Advanced PS 4 10 minutes
Slides from Lecture 5, with references 10 minutes

3 assignments Total 90 minutes

Problem Set 5 30 minutes
Quiz Week 5 30 minutes
Optional: Empirical Analysis of Network Data 30 minutes

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..

What's included

9 videos3 readings2 assignments

9 videos Total 100 minutes

6.1: Learning 26 minutes
6.2: DeGroot Model 16 minutes
6.3: Convergence in DeGroot Model 14 minutes
6.4: Proof of Convergence Theorem (Optional/Advanced 10:25) 10 minutes
6.5: Influence 7 minutes
6.6: Examples of Influence 8 minutes
6.7: Information Aggregation 9 minutes
6.8: Learning Summary 5 minutes
6.9: Week 6 Wrap 4 minutes

3 readings Total 30 minutes

Slides from Lecture 6, with references 10 minutes
OPTIONAL - Advanced Problem Set 6 10 minutes
OPTIONAL - Solutions to Advanced PS 5 10 minutes

2 assignments Total 60 minutes

Problem Set 6 30 minutes
Quiz Week 6 30 minutes

Network Games, Peer Influences: Strategic Complements and Substitutes, the Relation between Network Structure and Behavior, A Linear Quadratic Game, Repeated Interactions and Network Structures.

What's included

10 videos4 readings2 assignments

10 videos Total 141 minutes

7.1: Games on Networks 12 minutes
7.2: Complements and Substitutes 20 minutes
7.3: Properties of Equilibria 14 minutes
7.4: Multiple Equilibria 13 minutes
7.5: An Application 8 minutes
7.6: Beyond 0-1 Choices 21 minutes
7.7: A Linear Quadratic Model 15 minutes
7.8: RepeatedGames and Networks 24 minutes
7.9: Week 7 Wrap 5 minutes
7.9b: Course Wrap 10 minutes

4 readings Total 40 minutes

Slides from Lecture 7, with references 10 minutes
OPTIONAL - Advanced Problem Set 7 10 minutes
OPTIONAL - Solutions to Advanced PS 6 10 minutes
OPTIONAL - Solutions to Advanced PS 7 10 minutes

2 assignments Total 60 minutes

Problem Set 7 30 minutes
Quiz Week 7 30 minutes

The description goes here

What's included

1 assignment

Instructor

Instructor ratings

(114 ratings)

Matthew O. Jackson

Stanford University

3 Courses 641,059 learners

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Stanford University

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Reviewed on Jun 5, 2020

Interesting survey of modern network theory, from Erdos-Renyi random graphs, to SIS ("flu") models, and games on networks. Rather academic at times, without the rigour.

Reviewed on May 16, 2020

Matt is a fantastic instructor and has inspired many new ideas for my PhD project. Putting this course up here for free is extremely generous of both Matt and Coursera.

Reviewed on Dec 9, 2018

Excellent course. The labs are the best. Pajek and Gephi will be handy for network graphing and analyzing data. Thank you Professor Matthew Jackson. Your work is very good for reference.

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Social and Economic Networks: Models and Analysis

Social and Economic Networks: Models and Analysis

Skills you'll gain

Details to know

See how employees at top companies are mastering in-demand skills

There are 8 modules in this course

Introduction, Empirical Background and Definitions

What's included

Background, Definitions, and Measures Continued

What's included

Random Networks

What's included

Strategic Network Formation

What's included

Diffusion on Networks

What's included

Learning on Networks

What's included

Games on Networks

What's included

Final Exam

What's included

Instructor

Offered by

Explore more from Economics

Network Dynamics of Social Behavior

Social Network Analysis

Social Network Analysis

Network Modeling and Analysis in Python

Why people choose Coursera for their career

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

Learner reviews

Open new doors with Coursera Plus

Advance your career with an online degree

Join over 3,400 global companies that choose Coursera for Business

Frequently asked questions

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