Social and Economic Networks: Models and Analysis

4.8
stars
604 ratings

Matthew O. Jackson

Offered By

Social and Economic Networks: Models and Analysis

Stanford University

About 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

Learner Career Outcomes

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started a new career after completing these courses

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Flexible deadlines

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Advanced Level

Approx. 30 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish

Skills you will gain

Social NetworkGame TheoryNetwork AnalysisNetwork Theory

Learner Career Outcomes

20%

started a new career after completing these courses

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 30 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish

Instructor

Instructor rating

4.89/5 (48 Ratings)

Matthew O. Jackson

Offered by

Stanford University

The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

Syllabus - What you will learn from this course

Content Rating96%(3,280 ratings)

Week

Week 1

4 hours to complete

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

4 hours to complete

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

12 videos

An Introduction to the Course4m

1.1: Introduction9m

1.2: Examples and Challenges 15m

1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)8m

1.3: Definitions and Notation 14m

1.4: Diameter 16m

1.5: Diameter and Trees 6m

1.6: Diameters of Random Graphs (Optional/Advanced 11:12)11m

1.7: Diameters in the World 6m

1.8: Degree Distributions 13m

1.9: Clustering 8m

1.10: Week 1 Wrap2m

3 readings

Syllabus10m

Slides from Lecture 1, with References10m

OPTIONAL - Advanced Problem Set 110m

3 practice exercises

Quiz Week 130m

Problem Set 130m

Optional: Empirical Analysis of Network Data using Gephi or Pajek30m

Week

Week 2

4 hours to complete

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

4 hours to complete

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

11 videos

2.1: Homophily10m

2.2: Dynamics and Tie Strength 6m

2.3: Centrality Measures 14m

2.4: Centrality – Eigenvector Measures 13m

2.5a: Application - Centrality Measures 12m

2.5b: Application – Diffusion Centrality 6m

2.6: Random Networks 10m

2.7: Random Networks - Thresholds and Phase Transitions 7m

2.8: A Threshold Theorem (optional/advanced 13:00)13m

2.9: A Small World Model 7m

2.10 Week 2 Wrap3m

3 readings

Slides from Lecture 2, with references10m

OPTIONAL - Advanced Problem Set 210m

OPTIONAL - Solutions to Advanced PS 110m

3 practice exercises

Quiz Week 230m

Problem Set 230m

Optional: Empirical Analysis of Network Data30m

Week

Week 3

5 hours to complete

Random Networks

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

5 hours to complete

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

12 videos

3.1: Growing Random Networks17m

3.2: Mean Field Approximations 8m

3.3: Preferential Attachment 10m

3.4: Hybrid Models 14m

3.5: Fitting Hybrid Models 17m

3.6: Block Models 9m

3.7: ERGMs 9m

3.8: Estimating ERGMs 15m

3.9: SERGMs 9m

3.10: SUGMs 6m

3.11: Estimating SUGMs (Optional/Advanced 21:03)21m

3.12: Week 3 Wrap3m

3 readings

Slides from Lecture 3, with references10m

OPTIONAL - Advanced Problem Set 310m

OPTIONAL - Solutions to Advanced PS 210m

4 practice exercises

Quiz Week 330m

Problem Set 330m

Optional: Empirical Analysis of Network Data30m

Optional: Using Statnet in R to Estimate an ERGM30m

Week

Week 4

5 hours to complete

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.

5 hours to complete

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

15 videos

4.1: Strategic Network Formation12m

4.2: Pairwise Stability and Efficiency 15m

4.3: Connections Model 11m

4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)12m

4.5: Pairwise Stability in the Connections Model 6m

4.6: Externalities and the Coauthor Model 11m

4.7: Network Formation and Transfers 16m

4.8: Heterogeneity in Strategic Models 13m

4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)13m

4.10: Pairwise Nash Stability (Optional/Advanced 11:34)11m

4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)11m

4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)16m

4.13: Directed Network Formation (Optional/Advanced 16:38)16m

4.14: Application Structural Model (Optional/Advanced 35:06)35m

4.15: Week 4 Wrap4m

3 readings

Slides from Lecture 4, with references10m

OPTIONAL - Advanced Problem Set 410m

OPTIONAL - Solutions to Advanced PS 310m

2 practice exercises

Quiz Week 430m

Problem Set 430m

Week

Week 5

5 hours to complete

Diffusion on Networks

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

5 hours to complete

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

12 videos

5.1: Diffusion12m

5.2: Bass Model12m

5.3: Diffusion on Random Networks 9m

5.4: Giant Component Poisson Case 15m

5.5: SIS Model17m

5.6: Solving the SIS Model 9m

5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16)24m

5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47)22m

5.8b: Application: Financial Contagions (Optional/Advanced 12:47)12m

5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41)13m

5.9: Diffusion Summary 3m

5.10: Week 5 Wrap4m

3 readings

OPTIONAL - Advanced Problem Set 510m

OPTIONAL - Solutions to Advanced PS 410m

Slides from Lecture 5, with references10m

3 practice exercises

Quiz Week 530m

Problem Set 530m

Optional: Empirical Analysis of Network Data30m

Week

Week 6

3 hours to complete

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..

3 hours to complete

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

9 videos

6.1: Learning26m

6.2: DeGroot Model 15m

6.3: Convergence in DeGroot Model 13m

6.4: Proof of Convergence Theorem (Optional/Advanced 10:25)10m

6.5: Influence 6m

6.6: Examples of Influence 8m

6.7: Information Aggregation 9m

6.8: Learning Summary 4m

6.9: Week 6 Wrap4m

3 readings

Slides from Lecture 6, with references10m

OPTIONAL - Advanced Problem Set 610m

OPTIONAL - Solutions to Advanced PS 510m

2 practice exercises

Quiz Week 630m

Problem Set 630m

Week

Week 7

4 hours to complete

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.

4 hours to complete

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

10 videos

7.1: Games on Networks12m

7.2: Complements and Substitutes 19m

7.3: Properties of Equilibria 14m

7.4: Multiple Equilibria 13m

7.5: An Application 7m

7.6: Beyond 0-1 Choices 20m

7.7: A Linear Quadratic Model 14m

7.8: RepeatedGames and Networks 24m

7.9: Week 7 Wrap 4m

7.9b: Course Wrap10m

4 readings

Slides from Lecture 7, with references10m

OPTIONAL - Advanced Problem Set 710m

OPTIONAL - Solutions to Advanced PS 610m

OPTIONAL - Solutions to Advanced PS 710m

2 practice exercises

Quiz Week 730m

Problem Set 730m

Week

Week 8

1 hour to complete

Final Exam

The description goes here

1 hour to complete

Reviews

4.8

135 reviews

5 stars
84.13%
4 stars
13.38%
3 stars
1.48%
2 stars
0.16%
1 star
0.82%

TOP REVIEWS FROM SOCIAL AND ECONOMIC NETWORKS: MODELS AND ANALYSIS

by MRNov 1, 2017

Really enjoyed this course. The professor is really good and covers quite a lot of ground during the lectures. Good way to get into complex networks! Probably gonna do some studying on my own now :)

by SBOct 10, 2020

Very important course. My suggestion to the Prof. if he can increase the course length and include more details that would be much better or he can come up with advance course on the same series.

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.

by JKDec 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.

View all reviews

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Social and Economic Networks: Models and Analysis

Social and Economic Networks: Models and Analysis

About this Course

Learner Career Outcomes

20%

Skills you will gain

Learner Career Outcomes

20%

Instructor

Matthew O. Jackson

Offered by

Stanford University

Syllabus - What you will learn from this course

Week 1

Introduction, Empirical Background and Definitions

Week 2

Background, Definitions, and Measures Continued

Week 3

Random Networks

Week 4

Strategic Network Formation

Week 5

Diffusion on Networks

Week 6

Learning on Networks

Week 7

Games on Networks

Week 8

Final Exam

Reviews

TOP REVIEWS FROM SOCIAL AND ECONOMIC NETWORKS: MODELS AND ANALYSIS

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Social and Economic Networks: Models and Analysis

Social and Economic Networks: Models and Analysis

About this Course

Learner Career Outcomes

20%

Skills you will gain

Learner Career Outcomes

20%

Instructor

Matthew O. Jackson

Offered by

Stanford University

Syllabus - What you will learn from this course

Week 1

Introduction, Empirical Background and Definitions

Week 2

Background, Definitions, and Measures Continued

Week 3

Random Networks

Week 4

Strategic Network Formation

Week 5

Diffusion on Networks

Week 6

Learning on Networks

Week 7

Games on Networks

Week 8

Final Exam

Reviews

TOP REVIEWS FROM SOCIAL AND ECONOMIC NETWORKS: MODELS AND ANALYSIS

Frequently Asked Questions

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Top Online Courses

Top Online Specializations

Online Certificates

Online Degree Programs

Coursera

Community

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