About this Course

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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

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Advanced Level

Approx. 29 hours to complete

English

Skills you will gain

Social Network
Game Theory
Network Analysis
Network Theory

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Advanced Level

Approx. 29 hours to complete

English

Instructor

Instructor rating

4.88/5 (89 Ratings)

Matthew O. Jackson

Syllabus - What you will learn from this course

Content Rating97%(3,688 ratings)

Week1

Week 1

4 hours to complete

Introduction, Empirical Background and Definitions

4 hours to complete

12 videos (Total 118 min), 3 readings, 3 quizzes

12 videos

An Introduction to the Course4m1.1: Introduction9m1.2: Examples and Challenges 15m1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)8m1.3: Definitions and Notation 14m1.4: Diameter 16m1.5: Diameter and Trees 6m1.6: Diameters of Random Graphs (Optional/Advanced 11:12)11m1.7: Diameters in the World 6m1.8: Degree Distributions 13m1.9: Clustering 8m1.10: Week 1 Wrap2m

3 readings

Syllabus10mSlides from Lecture 1, with References10mOPTIONAL - Advanced Problem Set 110m

3 practice exercises

Quiz Week 130mProblem Set 130mOptional: Empirical Analysis of Network Data using Gephi or Pajek30m

Week2

Week 2

4 hours to complete

Background, Definitions, and Measures Continued

4 hours to complete

11 videos (Total 105 min), 3 readings, 3 quizzes

11 videos

2.1: Homophily10m2.2: Dynamics and Tie Strength 6m2.3: Centrality Measures 14m2.4: Centrality – Eigenvector Measures 13m2.5a: Application - Centrality Measures 12m2.5b: Application – Diffusion Centrality 6m2.6: Random Networks 10m2.7: Random Networks - Thresholds and Phase Transitions 7m2.8: A Threshold Theorem (optional/advanced 13:00)13m2.9: A Small World Model 7m2.10 Week 2 Wrap3m

3 readings

Slides from Lecture 2, with references10mOPTIONAL - Advanced Problem Set 210mOPTIONAL - Solutions to Advanced PS 110m

3 practice exercises

Quiz Week 230mProblem Set 230mOptional: Empirical Analysis of Network Data30m

Week3

Week 3

5 hours to complete

Random Networks

5 hours to complete

12 videos (Total 143 min), 3 readings, 4 quizzes

12 videos

3.1: Growing Random Networks17m3.2: Mean Field Approximations 8m3.3: Preferential Attachment 10m3.4: Hybrid Models 14m3.5: Fitting Hybrid Models 17m3.6: Block Models 9m3.7: ERGMs 9m3.8: Estimating ERGMs 15m3.9: SERGMs 9m3.10: SUGMs 6m3.11: Estimating SUGMs (Optional/Advanced 21:03)21m3.12: Week 3 Wrap3m

3 readings

Slides from Lecture 3, with references10mOPTIONAL - Advanced Problem Set 310mOPTIONAL - Solutions to Advanced PS 210m

4 practice exercises

Quiz Week 330mProblem Set 330mOptional: Empirical Analysis of Network Data30mOptional: Using Statnet in R to Estimate an ERGM30m

Week4

Week 4

5 hours to complete

Strategic Network Formation

5 hours to complete

15 videos (Total 209 min), 3 readings, 2 quizzes

15 videos

4.1: Strategic Network Formation12m4.2: Pairwise Stability and Efficiency 15m4.3: Connections Model 11m4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)12m4.5: Pairwise Stability in the Connections Model 6m4.6: Externalities and the Coauthor Model 11m4.7: Network Formation and Transfers 16m4.8: Heterogeneity in Strategic Models 13m4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)13m4.10: Pairwise Nash Stability (Optional/Advanced 11:34)11m4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)11m4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)16m4.13: Directed Network Formation (Optional/Advanced 16:38)16m4.14: Application Structural Model (Optional/Advanced 35:06)35m4.15: Week 4 Wrap4m

3 readings

Slides from Lecture 4, with references10mOPTIONAL - Advanced Problem Set 410mOPTIONAL - Solutions to Advanced PS 310m

2 practice exercises

Quiz Week 430mProblem Set 430m

Week5

Week 5

5 hours to complete

Diffusion on Networks

5 hours to complete

12 videos (Total 158 min), 3 readings, 3 quizzes

12 videos

5.1: Diffusion12m5.2: Bass Model12m5.3: Diffusion on Random Networks 9m5.4: Giant Component Poisson Case 15m5.5: SIS Model17m5.6: Solving the SIS Model 9m5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16)24m5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47)22m5.8b: Application: Financial Contagions (Optional/Advanced 12:47)12m5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41)13m5.9: Diffusion Summary 3m5.10: Week 5 Wrap4m

3 readings

OPTIONAL - Advanced Problem Set 510mOPTIONAL - Solutions to Advanced PS 410mSlides from Lecture 5, with references10m

3 practice exercises

Quiz Week 530mProblem Set 530mOptional: Empirical Analysis of Network Data30m

Week6

Week 6

3 hours to complete

Learning on Networks

3 hours to complete

9 videos (Total 100 min), 3 readings, 2 quizzes

9 videos

6.1: Learning26m6.2: DeGroot Model 15m6.3: Convergence in DeGroot Model 13m6.4: Proof of Convergence Theorem (Optional/Advanced 10:25)10m6.5: Influence 6m6.6: Examples of Influence 8m6.7: Information Aggregation 9m6.8: Learning Summary 4m6.9: Week 6 Wrap4m

3 readings

Slides from Lecture 6, with references10mOPTIONAL - Advanced Problem Set 610mOPTIONAL - Solutions to Advanced PS 510m

2 practice exercises

Quiz Week 630mProblem Set 630m

Week7

Week 7

4 hours to complete

Games on Networks

4 hours to complete

10 videos (Total 141 min), 4 readings, 2 quizzes

10 videos

7.1: Games on Networks12m7.2: Complements and Substitutes 19m7.3: Properties of Equilibria 14m7.4: Multiple Equilibria 13m7.5: An Application 7m7.6: Beyond 0-1 Choices 20m7.7: A Linear Quadratic Model 14m7.8: RepeatedGames and Networks 24m7.9: Week 7 Wrap 4m7.9b: Course Wrap10m

4 readings

Slides from Lecture 7, with references10mOPTIONAL - Advanced Problem Set 710mOPTIONAL - Solutions to Advanced PS 610mOPTIONAL - Solutions to Advanced PS 710m

2 practice exercises

Quiz Week 730mProblem Set 730m

Week8

Week 8

1 hour to complete

Final Exam

1 hour to complete

Reviews

4.8

162 reviews

5 stars
85.10%
4 stars
12.46%
3 stars
1.57%
2 stars
0.14%
1 star
0.71%

TOP REVIEWS FROM SOCIAL AND ECONOMIC NETWORKS: MODELS AND ANALYSIS

by ABApr 21, 2021

Very well done and explained, full of insight in the social network analysis!!! Lots of ideas about using it in company and team behaviours! Economical analysis of financial contagion is insightful!!!

by CTFeb 2, 2020

Professor Jackson is very clear and insightful! Loved his style of teaching. I hope more Economics professors were such good teachers as him.

by EAMay 14, 2017

Fantastic course! Compelling examples of application and an enthusiasm for the concepts that is contagious. Would love more from Prof. Jackson!

by SWAug 8, 2016

Very good course on Social Networks, and also a hard one even for graduate level. Generally assignments are not too tough but fully understanding all the concepts take lots of extra readings.

View all reviews

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Social and Economic Networks: Models and Analysis