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.
Offered By
Social and Economic Networks: Models and Analysis
Stanford UniversityAbout this Course
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Learner Career Outcomes
25%
started a new career after completing these courses
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. 25 hours to complete
English
Subtitles: English
Skills you will gain
Social NetworkGame TheoryNetwork AnalysisNetwork Theory
Learner Career Outcomes
25%
started a new career after completing these courses
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. 25 hours to complete
English
Subtitles: English
Offered by
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 - What you will learn from this course
3 hours to complete
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
3 hours to complete
12 videos (Total 118 min), 3 readings, 3 quizzes
12 videos
1.1: Introduction9m
1.2: Examples and Challenges 15m
1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)8m
1.3: Definitions and Notation 14m
1.4: Diameter 16m
1.5: Diameter and Trees 6m
1.6: Diameters of Random Graphs (Optional/Advanced 11:12)11m
1.7: Diameters in the World 6m
1.8: Degree Distributions 13m
1.9: Clustering 8m
1.10: Week 1 Wrap2m
3 readings
Syllabus10m
Slides from Lecture 1, with References10m
OPTIONAL - Advanced Problem Set 110m
3 practice exercises
Quiz Week 128m
Problem Set 112m
Optional: Empirical Analysis of Network Data using Gephi or Pajek8m
3 hours to complete
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
3 hours to complete
11 videos (Total 105 min), 3 readings, 3 quizzes
11 videos
2.2: Dynamics and Tie Strength 6m
2.3: Centrality Measures 14m
2.4: Centrality – Eigenvector Measures 13m
2.5a: Application - Centrality Measures 12m
2.5b: Application – Diffusion Centrality 6m
2.6: Random Networks 10m
2.7: Random Networks - Thresholds and Phase Transitions 7m
2.8: A Threshold Theorem (optional/advanced 13:00)13m
2.9: A Small World Model 7m
2.10 Week 2 Wrap3m
3 readings
Slides from Lecture 2, with references10m
OPTIONAL - Advanced Problem Set 210m
OPTIONAL - Solutions to Advanced PS 110m
3 practice exercises
Quiz Week 216m
Problem Set 210m
Optional: Empirical Analysis of Network Data6m
4 hours to complete
Random Networks
Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation.
4 hours to complete
12 videos (Total 143 min), 3 readings, 4 quizzes
12 videos
3.2: Mean Field Approximations 8m
3.3: Preferential Attachment 10m
3.4: Hybrid Models 14m
3.5: Fitting Hybrid Models 17m
3.6: Block Models 9m
3.7: ERGMs 9m
3.8: Estimating ERGMs 15m
3.9: SERGMs 9m
3.10: SUGMs 6m
3.11: Estimating SUGMs (Optional/Advanced 21:03)21m
3.12: Week 3 Wrap3m
3 readings
Slides from Lecture 3, with references10m
OPTIONAL - Advanced Problem Set 310m
OPTIONAL - Solutions to Advanced PS 210m
4 practice exercises
Quiz Week 326m
Problem Set 36m
Optional: Empirical Analysis of Network Data4m
Optional: Using Statnet in R to Estimate an ERGM6m
5 hours to complete
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.
5 hours to complete
15 videos (Total 209 min), 3 readings, 2 quizzes
15 videos
4.2: Pairwise Stability and Efficiency 15m
4.3: Connections Model 11m
4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)12m
4.5: Pairwise Stability in the Connections Model 6m
4.6: Externalities and the Coauthor Model 11m
4.7: Network Formation and Transfers 16m
4.8: Heterogeneity in Strategic Models 13m
4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)13m
4.10: Pairwise Nash Stability (Optional/Advanced 11:34)11m
4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)11m
4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)16m
4.13: Directed Network Formation (Optional/Advanced 16:38)16m
4.14: Application Structural Model (Optional/Advanced 35:06)35m
4.15: Week 4 Wrap4m
3 readings
Slides from Lecture 4, with references10m
OPTIONAL - Advanced Problem Set 410m
OPTIONAL - Solutions to Advanced PS 310m
2 practice exercises
Quiz Week 436m
Problem Set 414m
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