关于此课程： 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
此课程适用人群： 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.
制作方： Stanford University
教学方： Matthew O. Jackson, Professor
Economics
| 级别 | 高级设置 |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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 个视频, 3 个阅读材料, 2 个练习测验
- Reading: Syllabus
- 视频: 1.1: Introduction
- 视频: 1.2: Examples and Challenges
- 视频: 1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)
- 视频: 1.3: Definitions and Notation
- 视频: 1.4: Diameter
- 视频: 1.5: Diameter and Trees
- 视频: 1.6: Diameters of Random Graphs (Optional/Advanced 11:12)
- 视频: 1.7: Diameters in the World
- 视频: 1.8: Degree Distributions
- 视频: 1.9: Clustering
- 视频: 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
已评分: Problem Set 1
第 2 周
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 个视频, 3 个阅读材料, 2 个练习测验
- 视频: 2.2: Dynamics and Tie Strength
- 视频: 2.3: Centrality Measures
- 视频: 2.4: Centrality – Eigenvector Measures
- 视频: 2.5a: Application - Centrality Measures
- 视频: 2.5b: Application – Diffusion Centrality
- 视频: 2.6: Random Networks
- 视频: 2.7: Random Networks - Thresholds and Phase Transitions
- 视频: 2.8: A Threshold Theorem (optional/advanced 13:00)
- 视频: 2.9: A Small World Model
- 视频: 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
已评分: Problem Set 2
第 3 周
Random Networks
Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation.
12 个视频, 3 个阅读材料, 3 个练习测验
- 视频: 3.2: Mean Field Approximations
- 视频: 3.3: Preferential Attachment
- 视频: 3.4: Hybrid Models
- 视频: 3.5: Fitting Hybrid Models
- 视频: 3.6: Block Models
- 视频: 3.7: ERGMs
- 视频: 3.8: Estimating ERGMs
- 视频: 3.9: SERGMs
- 视频: 3.10: SUGMs
- 视频: 3.11: Estimating SUGMs (Optional/Advanced 21:03)
- 视频: 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
已评分: Problem Set 3
第 4 周
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 个视频, 3 个阅读材料, 1 个练习测验
- 视频: 4.2: Pairwise Stability and Efficiency
- 视频: 4.3: Connections Model
- 视频: 4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)
- 视频: 4.5: Pairwise Stability in the Connections Model
- 视频: 4.6: Externalities and the Coauthor Model
- 视频: 4.7: Network Formation and Transfers
- 视频: 4.8: Heterogeneity in Strategic Models
- 视频: 4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)
- 视频: 4.10: Pairwise Nash Stability (Optional/Advanced 11:34)
- 视频: 4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)
- 视频: 4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)
- 视频: 4.13: Directed Network Formation (Optional/Advanced 16:38)
- 视频: 4.14: Application Structural Model (Optional/Advanced 35:06)
- 视频: 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
已评分: Problem Set 4
第 5 周
Diffusion on Networks
Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data.
12 个视频, 3 个阅读材料, 2 个练习测验
- 视频: 5.2: Bass Model
- 视频: 5.3: Diffusion on Random Networks
- 视频: 5.4: Giant Component Poisson Case
- 视频: 5.5: SIS Model
- 视频: 5.6: Solving the SIS Model
- 视频: 5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16)
- 视频: 5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47)
- 视频: 5.8b: Application: Financial Contagions (Optional/Advanced 12:47)
- 视频: 5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41)
- 视频: 5.9: Diffusion Summary
- 视频: 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
已评分: Problem Set 5
第 6 周
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 个视频, 3 个阅读材料, 1 个练习测验
- 视频: 6.2: DeGroot Model
- 视频: 6.3: Convergence in DeGroot Model
- 视频: 6.4: Proof of Convergence Theorem (Optional/Advanced 10:25)
- 视频: 6.5: Influence
- 视频: 6.6: Examples of Influence
- 视频: 6.7: Information Aggregation
- 视频: 6.8: Learning Summary
- 视频: 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
已评分: Problem Set 6
第 7 周
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 个视频, 4 个阅读材料, 1 个练习测验
- 视频: 7.2: Complements and Substitutes
- 视频: 7.3: Properties of Equilibria
- 视频: 7.4: Multiple Equilibria
- 视频: 7.5: An Application
- 视频: 7.6: Beyond 0-1 Choices
- 视频: 7.7: A Linear Quadratic Model
- 视频: 7.8: RepeatedGames and Networks
- 视频: 7.9: Week 7 Wrap
- 视频: 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
已评分: Problem Set 7
第 8 周
Final Exam
The description goes here
已评分: Final
常见问题解答
运作方式
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
制作方
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.
评分和审阅
Very interesting and applicable subject matter taught in a very engaging way. Prof. O'Jackson is clearly incredibly capable in network analysis and game theory and he teaches the concepts in a remarkably concise and understandable manner.
Very interesting and well presented course .
An excellent walkthrough of the literature. I just wish there were more empirical exercises and and more hands-on work with the data and algorithms.
Greatly insightful and resourceful content for future research. As a recent university graduate interested in graduate school I found the course challenging meaning determination and consistency contributed to the successful completion of the course. Rewatching lectures and seeking external support helped me comprehend concepts through application.