Social and Economic Networks: Models and Analysis

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

Who is this class for: The course is aimed at people interested in researching social and economic networks, but should be accessible to advanced undergraduates and other people who have some prerequisites in mathematics and statistics. For example, it will be assumed that students are comfortable with basic concepts from linear algebra (e.g., matrix multiplication), probability theory (e.g., probability distributions, expected values, Bayes' rule), and statistics (e.g., hypothesis testing). Beyond those concepts, the course is self-contained.

Created by:  Stanford University

• Taught by:  Matthew O. Jackson, Professor

  Economics

Level Advanced Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.8 stars Average User Rating 4.8See what learners said Coursework

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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


WEEK 1


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


12 videos, 3 readings, 2 practice quizzes expand

1. Video: An Introduction to the Course
2. Reading: Syllabus
3. Video: 1.1: Introduction
4. Video: 1.2: Examples and Challenges
5. Video: 1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)
6. Video: 1.3: Definitions and Notation
7. Video: 1.4: Diameter
8. Video: 1.5: Diameter and Trees
9. Video: 1.6: Diameters of Random Graphs (Optional/Advanced 11:12)
10. Video: 1.7: Diameters in the World
11. Video: 1.8: Degree Distributions
12. Video: 1.9: Clustering
13. Video: 1.10: Week 1 Wrap
14. Practice Quiz: Quiz Week 1
15. Practice Quiz: Optional: Empirical Analysis of Network Data using Gephi or Pajek
16. Reading: Slides from Lecture 1, with References
17. Reading: OPTIONAL - Advanced Problem Set 1

Graded: Problem Set 1

WEEK 2


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


11 videos, 3 readings, 2 practice quizzes expand

1. Video: 2.1: Homophily
2. Video: 2.2: Dynamics and Tie Strength
3. Video: 2.3: Centrality Measures
4. Video: 2.4: Centrality – Eigenvector Measures
5. Video: 2.5a: Application - Centrality Measures
6. Video: 2.5b: Application – Diffusion Centrality
7. Video: 2.6: Random Networks
8. Video: 2.7: Random Networks - Thresholds and Phase Transitions
9. Video: 2.8: A Threshold Theorem (optional/advanced 13:00)
10. Video: 2.9: A Small World Model
11. Video: 2.10 Week 2 Wrap
12. Practice Quiz: Quiz Week 2
13. Practice Quiz: Optional: Empirical Analysis of Network Data
14. Reading: Slides from Lecture 2, with references
15. Reading: OPTIONAL - Advanced Problem Set 2
16. Reading: OPTIONAL - Solutions to Advanced PS 1

Graded: Problem Set 2

WEEK 3


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


12 videos, 3 readings, 3 practice quizzes expand

1. Video: 3.1: Growing Random Networks
2. Video: 3.2: Mean Field Approximations
3. Video: 3.3: Preferential Attachment
4. Video: 3.4: Hybrid Models
5. Video: 3.5: Fitting Hybrid Models
6. Video: 3.6: Block Models
7. Video: 3.7: ERGMs
8. Video: 3.8: Estimating ERGMs
9. Video: 3.9: SERGMs
10. Video: 3.10: SUGMs
11. Video: 3.11: Estimating SUGMs (Optional/Advanced 21:03)
12. Video: 3.12: Week 3 Wrap
13. Practice Quiz: Quiz Week 3
14. Practice Quiz: Optional: Empirical Analysis of Network Data
15. Practice Quiz: Optional: Using Statnet in R to Estimate an ERGM
16. Reading: Slides from Lecture 3, with references
17. Reading: OPTIONAL - Advanced Problem Set 3
18. Reading: OPTIONAL - Solutions to Advanced PS 2

Graded: Problem Set 3

WEEK 4


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.


15 videos, 3 readings, 1 practice quiz expand

1. Video: 4.1: Strategic Network Formation
2. Video: 4.2: Pairwise Stability and Efficiency
3. Video: 4.3: Connections Model
4. Video: 4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)
5. Video: 4.5: Pairwise Stability in the Connections Model
6. Video: 4.6: Externalities and the Coauthor Model
7. Video: 4.7: Network Formation and Transfers
8. Video: 4.8: Heterogeneity in Strategic Models
9. Video: 4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)
10. Video: 4.10: Pairwise Nash Stability (Optional/Advanced 11:34)
11. Video: 4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)
12. Video: 4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)
13. Video: 4.13: Directed Network Formation (Optional/Advanced 16:38)
14. Video: 4.14: Application Structural Model (Optional/Advanced 35:06)
15. Video: 4.15: Week 4 Wrap
16. Practice Quiz: Quiz Week 4
17. Reading: Slides from Lecture 4, with references
18. Reading: OPTIONAL - Advanced Problem Set 4
19. Reading: OPTIONAL - Solutions to Advanced PS 3

Graded: Problem Set 4

WEEK 5


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


12 videos, 3 readings, 2 practice quizzes expand

1. Video: 5.1: Diffusion
2. Video: 5.2: Bass Model
3. Video: 5.3: Diffusion on Random Networks
4. Video: 5.4: Giant Component Poisson Case
5. Video: 5.5: SIS Model
6. Video: 5.6: Solving the SIS Model
7. Video: 5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16)
8. Video: 5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47)
9. Video: 5.8b: Application: Financial Contagions (Optional/Advanced 12:47)
10. Video: 5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41)
11. Video: 5.9: Diffusion Summary
12. Video: 5.10: Week 5 Wrap
13. Practice Quiz: Quiz Week 5
14. Practice Quiz: Optional: Empirical Analysis of Network Data
15. Reading: OPTIONAL - Advanced Problem Set 5
16. Reading: OPTIONAL - Solutions to Advanced PS 4
17. Reading: Slides from Lecture 5, with references

Graded: Problem Set 5

WEEK 6


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..


9 videos, 3 readings, 1 practice quiz expand

1. Video: 6.1: Learning
2. Video: 6.2: DeGroot Model
3. Video: 6.3: Convergence in DeGroot Model
4. Video: 6.4: Proof of Convergence Theorem (Optional/Advanced 10:25)
5. Video: 6.5: Influence
6. Video: 6.6: Examples of Influence
7. Video: 6.7: Information Aggregation
8. Video: 6.8: Learning Summary
9. Video: 6.9: Week 6 Wrap
10. Practice Quiz: Quiz Week 6
11. Reading: Slides from Lecture 6, with references
12. Reading: OPTIONAL - Advanced Problem Set 6
13. Reading: OPTIONAL - Solutions to Advanced PS 5

Graded: Problem Set 6

WEEK 7


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.


10 videos, 4 readings, 1 practice quiz expand

1. Video: 7.1: Games on Networks
2. Video: 7.2: Complements and Substitutes
3. Video: 7.3: Properties of Equilibria
4. Video: 7.4: Multiple Equilibria
5. Video: 7.5: An Application
6. Video: 7.6: Beyond 0-1 Choices
7. Video: 7.7: A Linear Quadratic Model
8. Video: 7.8: RepeatedGames and Networks
9. Video: 7.9: Week 7 Wrap
10. Video: 7.9b: Course Wrap
11. Practice Quiz: Quiz Week 7
12. Reading: Slides from Lecture 7, with references
13. Reading: OPTIONAL - Advanced Problem Set 7
14. Reading: OPTIONAL - Solutions to Advanced PS 6
15. Reading: OPTIONAL - Solutions to Advanced PS 7

Graded: Problem Set 7

WEEK 8


Final Exam
The description goes here

Graded: Final