À propos de ce cours : 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
Les destinataires de ce cours : 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.
Enseigné par : Matthew O. Jackson, Professor
Economics
| Niveau | Advanced |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
- Reading: Syllabus
- Vidéo: 1.1: Introduction
- Vidéo: 1.2: Examples and Challenges
- Vidéo: 1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)
- Vidéo: 1.3: Definitions and Notation
- Vidéo: 1.4: Diameter
- Vidéo: 1.5: Diameter and Trees
- Vidéo: 1.6: Diameters of Random Graphs (Optional/Advanced 11:12)
- Vidéo: 1.7: Diameters in the World
- Vidéo: 1.8: Degree Distributions
- Vidéo: 1.9: Clustering
- Vidéo: 1.10: Week 1 Wrap
- Practice Quiz: Quiz Week 1
- Practice Quiz: Optional: Empirical Analysis of Network Data using Gephi or Pajek
- Reading: Slides from Lecture 1, with References
- Reading: OPTIONAL - Advanced Problem Set 1
- Vidéo: 2.2: Dynamics and Tie Strength
- Vidéo: 2.3: Centrality Measures
- Vidéo: 2.4: Centrality – Eigenvector Measures
- Vidéo: 2.5a: Application - Centrality Measures
- Vidéo: 2.5b: Application – Diffusion Centrality
- Vidéo: 2.6: Random Networks
- Vidéo: 2.7: Random Networks - Thresholds and Phase Transitions
- Vidéo: 2.8: A Threshold Theorem (optional/advanced 13:00)
- Vidéo: 2.9: A Small World Model
- Vidéo: 2.10 Week 2 Wrap
- Practice Quiz: Quiz Week 2
- Practice Quiz: Optional: Empirical Analysis of Network Data
- Reading: Slides from Lecture 2, with references
- Reading: OPTIONAL - Advanced Problem Set 2
- Reading: OPTIONAL - Solutions to Advanced PS 1
- Vidéo: 3.2: Mean Field Approximations
- Vidéo: 3.3: Preferential Attachment
- Vidéo: 3.4: Hybrid Models
- Vidéo: 3.5: Fitting Hybrid Models
- Vidéo: 3.6: Block Models
- Vidéo: 3.7: ERGMs
- Vidéo: 3.8: Estimating ERGMs
- Vidéo: 3.9: SERGMs
- Vidéo: 3.10: SUGMs
- Vidéo: 3.11: Estimating SUGMs (Optional/Advanced 21:03)
- Vidéo: 3.12: Week 3 Wrap
- Practice Quiz: Quiz Week 3
- Practice Quiz: Optional: Empirical Analysis of Network Data
- Practice Quiz: Optional: Using Statnet in R to Estimate an ERGM
- Reading: Slides from Lecture 3, with references
- Reading: OPTIONAL - Advanced Problem Set 3
- Reading: OPTIONAL - Solutions to Advanced PS 2
- Vidéo: 4.2: Pairwise Stability and Efficiency
- Vidéo: 4.3: Connections Model
- Vidéo: 4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)
- Vidéo: 4.5: Pairwise Stability in the Connections Model
- Vidéo: 4.6: Externalities and the Coauthor Model
- Vidéo: 4.7: Network Formation and Transfers
- Vidéo: 4.8: Heterogeneity in Strategic Models
- Vidéo: 4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)
- Vidéo: 4.10: Pairwise Nash Stability (Optional/Advanced 11:34)
- Vidéo: 4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)
- Vidéo: 4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)
- Vidéo: 4.13: Directed Network Formation (Optional/Advanced 16:38)
- Vidéo: 4.14: Application Structural Model (Optional/Advanced 35:06)
- Vidéo: 4.15: Week 4 Wrap
- Practice Quiz: Quiz Week 4
- Reading: Slides from Lecture 4, with references
- Reading: OPTIONAL - Advanced Problem Set 4
- Reading: OPTIONAL - Solutions to Advanced PS 3
- Vidéo: 5.2: Bass Model
- Vidéo: 5.3: Diffusion on Random Networks
- Vidéo: 5.4: Giant Component Poisson Case
- Vidéo: 5.5: SIS Model
- Vidéo: 5.6: Solving the SIS Model
- Vidéo: 5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16)
- Vidéo: 5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47)
- Vidéo: 5.8b: Application: Financial Contagions (Optional/Advanced 12:47)
- Vidéo: 5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41)
- Vidéo: 5.9: Diffusion Summary
- Vidéo: 5.10: Week 5 Wrap
- Practice Quiz: Quiz Week 5
- Practice Quiz: Optional: Empirical Analysis of Network Data
- Reading: OPTIONAL - Advanced Problem Set 5
- Reading: OPTIONAL - Solutions to Advanced PS 4
- Reading: Slides from Lecture 5, with references
- Vidéo: 6.2: DeGroot Model
- Vidéo: 6.3: Convergence in DeGroot Model
- Vidéo: 6.4: Proof of Convergence Theorem (Optional/Advanced 10:25)
- Vidéo: 6.5: Influence
- Vidéo: 6.6: Examples of Influence
- Vidéo: 6.7: Information Aggregation
- Vidéo: 6.8: Learning Summary
- Vidéo: 6.9: Week 6 Wrap
- Practice Quiz: Quiz Week 6
- Reading: Slides from Lecture 6, with references
- Reading: OPTIONAL - Advanced Problem Set 6
- Reading: OPTIONAL - Solutions to Advanced PS 5
- Vidéo: 7.2: Complements and Substitutes
- Vidéo: 7.3: Properties of Equilibria
- Vidéo: 7.4: Multiple Equilibria
- Vidéo: 7.5: An Application
- Vidéo: 7.6: Beyond 0-1 Choices
- Vidéo: 7.7: A Linear Quadratic Model
- Vidéo: 7.8: RepeatedGames and Networks
- Vidéo: 7.9: Week 7 Wrap
- Vidéo: 7.9b: Course Wrap
- Practice Quiz: Quiz Week 7
- Reading: Slides from Lecture 7, with references
- Reading: OPTIONAL - Advanced Problem Set 7
- Reading: OPTIONAL - Solutions to Advanced PS 6
- Reading: OPTIONAL - Solutions to Advanced PS 7
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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.
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.