Social and Economic Networks: Models and Analysis

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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

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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 videosTotal 118 minutes

An Introduction to the Course4 minutes
1.1: Introduction9 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 Wrap3 minutes

3 readingsTotal 30 minutes

Syllabus10 minutes
Slides from Lecture 1, with References10 minutes
OPTIONAL - Advanced Problem Set 110 minutes

3 assignmentsTotal 90 minutes

Problem Set 130 minutes
Quiz Week 130 minutes
Optional: Empirical Analysis of Network Data using Gephi or Pajek30 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 videosTotal 105 minutes

2.1: Homophily10 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 Wrap4 minutes

3 readingsTotal 30 minutes

Slides from Lecture 2, with references10 minutes
OPTIONAL - Advanced Problem Set 210 minutes
OPTIONAL - Solutions to Advanced PS 110 minutes

3 assignmentsTotal 90 minutes

Problem Set 230 minutes
Quiz Week 230 minutes
Optional: Empirical Analysis of Network Data30 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 videosTotal 143 minutes

3.1: Growing Random Networks18 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 Wrap3 minutes

3 readingsTotal 30 minutes

Slides from Lecture 3, with references10 minutes
OPTIONAL - Advanced Problem Set 310 minutes
OPTIONAL - Solutions to Advanced PS 210 minutes

4 assignmentsTotal 120 minutes

Problem Set 330 minutes
Quiz Week 330 minutes
Optional: Empirical Analysis of Network Data30 minutes
Optional: Using Statnet in R to Estimate an ERGM30 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 videosTotal 209 minutes

4.1: Strategic Network Formation12 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 Wrap4 minutes

3 readingsTotal 30 minutes

Slides from Lecture 4, with references10 minutes
OPTIONAL - Advanced Problem Set 410 minutes
OPTIONAL - Solutions to Advanced PS 310 minutes

2 assignmentsTotal 60 minutes

Problem Set 430 minutes
Quiz Week 430 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 videosTotal 158 minutes

5.1: Diffusion13 minutes
5.2: Bass Model12 minutes
5.3: Diffusion on Random Networks 9 minutes
5.4: Giant Component Poisson Case 15 minutes
5.5: SIS Model18 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 Wrap4 minutes

3 readingsTotal 30 minutes

OPTIONAL - Advanced Problem Set 510 minutes
OPTIONAL - Solutions to Advanced PS 410 minutes
Slides from Lecture 5, with references10 minutes

3 assignmentsTotal 90 minutes

Problem Set 530 minutes
Quiz Week 530 minutes
Optional: Empirical Analysis of Network Data30 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 videosTotal 100 minutes

6.1: Learning26 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 Wrap4 minutes

3 readingsTotal 30 minutes

Slides from Lecture 6, with references10 minutes
OPTIONAL - Advanced Problem Set 610 minutes
OPTIONAL - Solutions to Advanced PS 510 minutes

2 assignmentsTotal 60 minutes

Problem Set 630 minutes
Quiz Week 630 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 videosTotal 141 minutes

7.1: Games on Networks12 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 Wrap10 minutes

4 readingsTotal 40 minutes

Slides from Lecture 7, with references10 minutes
OPTIONAL - Advanced Problem Set 710 minutes
OPTIONAL - Solutions to Advanced PS 610 minutes
OPTIONAL - Solutions to Advanced PS 710 minutes

2 assignmentsTotal 60 minutes

Problem Set 730 minutes
Quiz Week 730 minutes

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What's included

1 assignment

Instructor

Instructor ratings

(114 ratings)

Matthew O. Jackson

Stanford University

3 Courses643,725 learners

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Stanford University

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Reviewed on May 16, 2023

Matthew is a very good teacher. I felt like conversating on social behaviors and network structures while learning pretty complex statistical physics. Thank you

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.

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