Stanford University - Social and Economic Networks: Models and Analysis
- Offered byCoursera
Social and Economic Networks: Models and Analysis at Coursera Overview
Duration | 30 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Social and Economic Networks: Models and Analysis at Coursera Highlights
- 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), Italian, Vietnamese, German, Russian, English, Spanish
Social and Economic Networks: Models and Analysis at Coursera Course details
- 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
Social and Economic Networks: Models and Analysis at Coursera Curriculum
Introduction, Empirical Background and Definitions
An Introduction to the Course
1.1: Introduction
1.2: Examples and Challenges
1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)
1.3: Definitions and Notation
1.4: Diameter
1.5: Diameter and Trees
1.6: Diameters of Random Graphs (Optional/Advanced 11:12)
1.7: Diameters in the World
1.8: Degree Distributions
1.9: Clustering
1.10: Week 1 Wrap
Syllabus
Slides from Lecture 1, with References
OPTIONAL - Advanced Problem Set 1
Quiz Week 1
Problem Set 1
Optional: Empirical Analysis of Network Data using Gephi or Pajek
Background, Definitions, and Measures Continued
2.1: Homophily
2.2: Dynamics and Tie Strength
2.3: Centrality Measures
2.4: Centrality ? Eigenvector Measures
2.5a: Application - Centrality Measures
2.5b: Application ? Diffusion Centrality
2.6: Random Networks
2.7: Random Networks - Thresholds and Phase Transitions
2.8: A Threshold Theorem (optional/advanced 13:00)
2.9: A Small World Model
2.10 Week 2 Wrap
Slides from Lecture 2, with references
OPTIONAL - Advanced Problem Set 2
OPTIONAL - Solutions to Advanced PS 1
Quiz Week 2
Problem Set 2
Optional: Empirical Analysis of Network Data
Random Networks
3.1: Growing Random Networks
3.2: Mean Field Approximations
3.3: Preferential Attachment
3.4: Hybrid Models
3.5: Fitting Hybrid Models
3.6: Block Models
3.7: ERGMs
3.8: Estimating ERGMs
3.9: SERGMs
3.10: SUGMs
3.11: Estimating SUGMs (Optional/Advanced 21:03)
3.12: Week 3 Wrap
Slides from Lecture 3, with references
OPTIONAL - Advanced Problem Set 3
OPTIONAL - Solutions to Advanced PS 2
Quiz Week 3
Problem Set 3
Optional: Empirical Analysis of Network Data
Optional: Using Statnet in R to Estimate an ERGM
Strategic Network Formation
4.1: Strategic Network Formation
4.2: Pairwise Stability and Efficiency
4.3: Connections Model
4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)
4.5: Pairwise Stability in the Connections Model
4.6: Externalities and the Coauthor Model
4.7: Network Formation and Transfers
4.8: Heterogeneity in Strategic Models
4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)
4.10: Pairwise Nash Stability (Optional/Advanced 11:34)
4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)
4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)
4.13: Directed Network Formation (Optional/Advanced 16:38)
4.14: Application Structural Model (Optional/Advanced 35:06)
4.15: Week 4 Wrap
Slides from Lecture 4, with references
OPTIONAL - Advanced Problem Set 4
OPTIONAL - Solutions to Advanced PS 3
Quiz Week 4
Problem Set 4
Diffusion on Networks
5.1: Diffusion
5.2: Bass Model
5.3: Diffusion on Random Networks
5.4: Giant Component Poisson Case
5.5: SIS Model
5.6: Solving the SIS Model
5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16)
5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47)
5.8b: Application: Financial Contagions (Optional/Advanced 12:47)
5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41)
5.9: Diffusion Summary
5.10: Week 5 Wrap
OPTIONAL - Advanced Problem Set 5
OPTIONAL - Solutions to Advanced PS 4
Slides from Lecture 5, with references
Quiz Week 5
Problem Set 5
Optional: Empirical Analysis of Network Data
Learning on Networks
6.1: Learning
6.2: DeGroot Model
6.3: Convergence in DeGroot Model
6.4: Proof of Convergence Theorem (Optional/Advanced 10:25)
6.5: Influence
6.6: Examples of Influence
6.7: Information Aggregation
6.8: Learning Summary
6.9: Week 6 Wrap
Slides from Lecture 6, with references
OPTIONAL - Advanced Problem Set 6
OPTIONAL - Solutions to Advanced PS 5
Quiz Week 6
Problem Set 6
Games on Networks
7.1: Games on Networks
7.2: Complements and Substitutes
7.3: Properties of Equilibria
7.4: Multiple Equilibria
7.5: An Application
7.6: Beyond 0-1 Choices
7.7: A Linear Quadratic Model
7.8: RepeatedGames and Networks
7.9: Week 7 Wrap
7.9b: Course Wrap
Slides from Lecture 7, with references
OPTIONAL - Advanced Problem Set 7
OPTIONAL - Solutions to Advanced PS 6
OPTIONAL - Solutions to Advanced PS 7
Quiz Week 7
Problem Set 7
Final Exam
Final
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