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 |
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
- Video: An Introduction to the Course
- Leyendo: Syllabus
- Video: 1.1: Introduction
- Video: 1.2: Examples and Challenges
- Video: 1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)
- Video: 1.3: Definitions and Notation
- Video: 1.4: Diameter
- Video: 1.5: Diameter and Trees
- Video: 1.6: Diameters of Random Graphs (Optional/Advanced 11:12)
- Video: 1.7: Diameters in the World
- Video: 1.8: Degree Distributions
- Video: 1.9: Clustering
- Video: 1.10: Week 1 Wrap
- Cuestionario de práctica: Quiz Week 1
- Cuestionario de práctica: Optional: Empirical Analysis of Network Data using Gephi or Pajek
- Leyendo: Slides from Lecture 1, with References
- Leyendo: 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
- Video: 2.1: Homophily
- Video: 2.2: Dynamics and Tie Strength
- Video: 2.3: Centrality Measures
- Video: 2.4: Centrality – Eigenvector Measures
- Video: 2.5a: Application - Centrality Measures
- Video: 2.5b: Application – Diffusion Centrality
- Video: 2.6: Random Networks
- Video: 2.7: Random Networks - Thresholds and Phase Transitions
- Video: 2.8: A Threshold Theorem (optional/advanced 13:00)
- Video: 2.9: A Small World Model
- Video: 2.10 Week 2 Wrap
- Cuestionario de práctica: Quiz Week 2
- Cuestionario de práctica: Optional: Empirical Analysis of Network Data
- Leyendo: Slides from Lecture 2, with references
- Leyendo: OPTIONAL - Advanced Problem Set 2
- Leyendo: 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
- Video: 3.1: Growing Random Networks
- Video: 3.2: Mean Field Approximations
- Video: 3.3: Preferential Attachment
- Video: 3.4: Hybrid Models
- Video: 3.5: Fitting Hybrid Models
- Video: 3.6: Block Models
- Video: 3.7: ERGMs
- Video: 3.8: Estimating ERGMs
- Video: 3.9: SERGMs
- Video: 3.10: SUGMs
- Video: 3.11: Estimating SUGMs (Optional/Advanced 21:03)
- Video: 3.12: Week 3 Wrap
- Cuestionario de práctica: Quiz Week 3
- Cuestionario de práctica: Optional: Empirical Analysis of Network Data
- Cuestionario de práctica: Optional: Using Statnet in R to Estimate an ERGM
- Leyendo: Slides from Lecture 3, with references
- Leyendo: OPTIONAL - Advanced Problem Set 3
- Leyendo: 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
- Video: 4.1: Strategic Network Formation
- Video: 4.2: Pairwise Stability and Efficiency
- Video: 4.3: Connections Model
- Video: 4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)
- Video: 4.5: Pairwise Stability in the Connections Model
- Video: 4.6: Externalities and the Coauthor Model
- Video: 4.7: Network Formation and Transfers
- Video: 4.8: Heterogeneity in Strategic Models
- Video: 4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)
- Video: 4.10: Pairwise Nash Stability (Optional/Advanced 11:34)
- Video: 4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)
- Video: 4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)
- Video: 4.13: Directed Network Formation (Optional/Advanced 16:38)
- Video: 4.14: Application Structural Model (Optional/Advanced 35:06)
- Video: 4.15: Week 4 Wrap
- Cuestionario de práctica: Quiz Week 4
- Leyendo: Slides from Lecture 4, with references
- Leyendo: OPTIONAL - Advanced Problem Set 4
- Leyendo: 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
- Video: 5.1: Diffusion
- Video: 5.2: Bass Model
- Video: 5.3: Diffusion on Random Networks
- Video: 5.4: Giant Component Poisson Case
- Video: 5.5: SIS Model
- Video: 5.6: Solving the SIS Model
- Video: 5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16)
- Video: 5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47)
- Video: 5.8b: Application: Financial Contagions (Optional/Advanced 12:47)
- Video: 5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41)
- Video: 5.9: Diffusion Summary
- Video: 5.10: Week 5 Wrap
- Cuestionario de práctica: Quiz Week 5
- Cuestionario de práctica: Optional: Empirical Analysis of Network Data
- Leyendo: OPTIONAL - Advanced Problem Set 5
- Leyendo: OPTIONAL - Solutions to Advanced PS 4
- Leyendo: 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
- Video: 6.1: Learning
- Video: 6.2: DeGroot Model
- Video: 6.3: Convergence in DeGroot Model
- Video: 6.4: Proof of Convergence Theorem (Optional/Advanced 10:25)
- Video: 6.5: Influence
- Video: 6.6: Examples of Influence
- Video: 6.7: Information Aggregation
- Video: 6.8: Learning Summary
- Video: 6.9: Week 6 Wrap
- Cuestionario de práctica: Quiz Week 6
- Leyendo: Slides from Lecture 6, with references
- Leyendo: OPTIONAL - Advanced Problem Set 6
- Leyendo: 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
- Video: 7.1: Games on Networks
- Video: 7.2: Complements and Substitutes
- Video: 7.3: Properties of Equilibria
- Video: 7.4: Multiple Equilibria
- Video: 7.5: An Application
- Video: 7.6: Beyond 0-1 Choices
- Video: 7.7: A Linear Quadratic Model
- Video: 7.8: RepeatedGames and Networks
- Video: 7.9: Week 7 Wrap
- Video: 7.9b: Course Wrap
- Cuestionario de práctica: Quiz Week 7
- Leyendo: Slides from Lecture 7, with references
- Leyendo: OPTIONAL - Advanced Problem Set 7
- Leyendo: OPTIONAL - Solutions to Advanced PS 6
- Leyendo: OPTIONAL - Solutions to Advanced PS 7
Graded: Problem Set 7
WEEK 8
Final Exam
The description goes here
Graded: Final
FAQs
How It Works
Trabajo del curso
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Ayuda de tus compañeros
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Certificados
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Creators
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.
Ratings and Reviews
Prof. Jackson is an outstanding teacher, and I very much enjoyed this course. I come from a probability background (PhD) but never looked at graphs or networks before. I thought that the course was very well made, with a perfect balance between theoretical concepts and practical applications. I also think that Prof. Jackson's treatment of mathematical concepts is entirely optimal given the diverse audience he most likely has: it is technical, but definitely not going into the more formal details you would get in a math course. I think this is great, because for the more math-oriented people it's just an occasion to look up some references, or think about a more formal way of expressing the concepts in question, while it does not overwhelm those who don't want to go through a bunch of existence theorems. By all counts, an outstanding course.
Excellent
Wish we had projects to work on. Excellent theory material though.
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MG
Great survey course for social network analysis. Dr. Jackson's lectures motivated me to buy the book, and I hope to come back to this course later to work more on the optional parts.