Social and Economic Networks: Models and Analysis
proposé par
Social and Economic Networks: Models and Analysis
Stanford University
À propos de ce cours
4.8
331 ratings
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
Compétences que vous acquerrez
Network AnalysisSocial NetworkNetwork TheorySocial Network Analysis
Programme du cours : ce que vous apprendrez dans ce cours
1
Section
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 vidéos (Total 118 min), 3 lectures, 3 quiz
12 vidéos
1.1: Introduction9 min
1.2: Examples and Challenges 15 min
1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)8 min
1.3: Definitions and Notation 14 min
1.4: Diameter 16 min
1.5: Diameter and Trees 6 min
1.6: Diameters of Random Graphs (Optional/Advanced 11:12)11 min
1.7: Diameters in the World 6 min
1.8: Degree Distributions 13 min
1.9: Clustering 8 min
1.10: Week 1 Wrap2 min
3 lectures
Syllabus10 min
Slides from Lecture 1, with References10 min
OPTIONAL - Advanced Problem Set 110 min
3 exercices pour s'entraîner
Quiz Week 128 min
Problem Set 112 min
Optional: Empirical Analysis of Network Data using Gephi or Pajek8 min
2
Section
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 vidéos (Total 105 min), 3 lectures, 3 quiz
11 vidéos
2.1: Homophily10 min
2.2: Dynamics and Tie Strength 6 min
2.3: Centrality Measures 14 min
2.4: Centrality – Eigenvector Measures 13 min
2.5a: Application - Centrality Measures 12 min
2.5b: Application – Diffusion Centrality 6 min
2.6: Random Networks 10 min
2.7: Random Networks - Thresholds and Phase Transitions 7 min
2.8: A Threshold Theorem (optional/advanced 13:00)13 min
2.9: A Small World Model 7 min
2.10 Week 2 Wrap3 min
3 lectures
Slides from Lecture 2, with references10 min
OPTIONAL - Advanced Problem Set 210 min
OPTIONAL - Solutions to Advanced PS 110 min
3 exercices pour s'entraîner
Quiz Week 216 min
Problem Set 210 min
Optional: Empirical Analysis of Network Data6 min
3
Section
Random Networks
Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation.
12 vidéos (Total 143 min), 3 lectures, 4 quiz
12 vidéos
3.2: Mean Field Approximations 8 min
3.3: Preferential Attachment 10 min
3.4: Hybrid Models 14 min
3.5: Fitting Hybrid Models 17 min
3.6: Block Models 9 min
3.7: ERGMs 9 min
3.8: Estimating ERGMs 15 min
3.9: SERGMs 9 min
3.10: SUGMs 6 min
3.11: Estimating SUGMs (Optional/Advanced 21:03)21 min
3.12: Week 3 Wrap3 min
3 lectures
Slides from Lecture 3, with references10 min
OPTIONAL - Advanced Problem Set 310 min
OPTIONAL - Solutions to Advanced PS 210 min
4 exercices pour s'entraîner
Quiz Week 326 min
Problem Set 36 min
Optional: Empirical Analysis of Network Data4 min
Optional: Using Statnet in R to Estimate an ERGM6 min
4
Section
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 vidéos (Total 209 min), 3 lectures, 2 quiz
15 vidéos
4.2: Pairwise Stability and Efficiency 15 min
4.3: Connections Model 11 min
4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)12 min
4.5: Pairwise Stability in the Connections Model 6 min
4.6: Externalities and the Coauthor Model 11 min
4.7: Network Formation and Transfers 16 min
4.8: Heterogeneity in Strategic Models 13 min
4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)13 min
4.10: Pairwise Nash Stability (Optional/Advanced 11:34)11 min
4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)11 min
4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)16 min
4.13: Directed Network Formation (Optional/Advanced 16:38)16 min
4.14: Application Structural Model (Optional/Advanced 35:06)35 min
4.15: Week 4 Wrap4 min
3 lectures
Slides from Lecture 4, with references10 min
OPTIONAL - Advanced Problem Set 410 min
OPTIONAL - Solutions to Advanced PS 310 min
2 exercices pour s'entraîner
Quiz Week 436 min
Problem Set 414 min
4.8
Meilleurs avis
par SW•Aug 9th 2016
Very good course on Social Networks, and also a hard one even for graduate level. Generally assignments are not too tough but fully understanding all the concepts take lots of extra readings.
par MG•Apr 17th 2018
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.
Enseignant
Matthew O. Jackson
ProfessorEconomics
À propos de 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.
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