Social and Economic Networks: Models and Analysis

Instructor: Matthew O. Jackson

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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

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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

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

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

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

Quiz Week 4 30 minutes
Problem Set 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

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

Quiz Week 6 30 minutes
Problem Set 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

Quiz Week 7 30 minutes
Problem Set 7 30 minutes

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What's included

1 assignment

Instructor

Instructor ratings

(114 ratings)

Matthew O. Jackson

Stanford University

3 Courses 640,505 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 Nov 25, 2021

Really good course. This professor knows what he's talking about and is a person to aspire to become. I wish the mathematical notation was more standard and consistant.

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