Social and Economic Networks: Models and Analysis
oferecido por
Social and Economic Networks: Models and Analysis
Stanford University
Informações sobre o curso
4.8
333 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
Habilidades que você terá
Network AnalysisSocial NetworkNetwork TheoryGame Theory
Programa - O que você aprenderá com este curso
1
Seção
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 vídeos (Total de 118 min), 3 leituras, 3 testes
12 videos
1.1: Introduction9min
1.2: Examples and Challenges 15min
1.2.5 Background Definitions and Notation (Basic - Skip if familiar 8:23)8min
1.3: Definitions and Notation 14min
1.4: Diameter 16min
1.5: Diameter and Trees 6min
1.6: Diameters of Random Graphs (Optional/Advanced 11:12)11min
1.7: Diameters in the World 6min
1.8: Degree Distributions 13min
1.9: Clustering 8min
1.10: Week 1 Wrap2min
3 leituras
Syllabus10min
Slides from Lecture 1, with References10min
OPTIONAL - Advanced Problem Set 110min
3 exercícios práticos
Quiz Week 128min
Problem Set 112min
Optional: Empirical Analysis of Network Data using Gephi or Pajek8min
2
Seção
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 vídeos (Total de 105 min), 3 leituras, 3 testes
11 videos
2.1: Homophily10min
2.2: Dynamics and Tie Strength 6min
2.3: Centrality Measures 14min
2.4: Centrality – Eigenvector Measures 13min
2.5a: Application - Centrality Measures 12min
2.5b: Application – Diffusion Centrality 6min
2.6: Random Networks 10min
2.7: Random Networks - Thresholds and Phase Transitions 7min
2.8: A Threshold Theorem (optional/advanced 13:00)13min
2.9: A Small World Model 7min
2.10 Week 2 Wrap3min
3 leituras
Slides from Lecture 2, with references10min
OPTIONAL - Advanced Problem Set 210min
OPTIONAL - Solutions to Advanced PS 110min
3 exercícios práticos
Quiz Week 216min
Problem Set 210min
Optional: Empirical Analysis of Network Data6min
3
Seção
Random Networks
Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation.
12 vídeos (Total de 143 min), 3 leituras, 4 testes
12 videos
3.2: Mean Field Approximations 8min
3.3: Preferential Attachment 10min
3.4: Hybrid Models 14min
3.5: Fitting Hybrid Models 17min
3.6: Block Models 9min
3.7: ERGMs 9min
3.8: Estimating ERGMs 15min
3.9: SERGMs 9min
3.10: SUGMs 6min
3.11: Estimating SUGMs (Optional/Advanced 21:03)21min
3.12: Week 3 Wrap3min
3 leituras
Slides from Lecture 3, with references10min
OPTIONAL - Advanced Problem Set 310min
OPTIONAL - Solutions to Advanced PS 210min
4 exercícios práticos
Quiz Week 326min
Problem Set 36min
Optional: Empirical Analysis of Network Data4min
Optional: Using Statnet in R to Estimate an ERGM6min
4
Seção
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 vídeos (Total de 209 min), 3 leituras, 2 testes
15 videos
4.2: Pairwise Stability and Efficiency 15min
4.3: Connections Model 11min
4.4: Efficiency in the Connections Model (Optional/Advanced 12:41)12min
4.5: Pairwise Stability in the Connections Model 6min
4.6: Externalities and the Coauthor Model 11min
4.7: Network Formation and Transfers 16min
4.8: Heterogeneity in Strategic Models 13min
4.9: SUGMs and Strategic Network Formation (Optional/Advanced 13:47)13min
4.10: Pairwise Nash Stability (Optional/Advanced 11:34)11min
4.11: Dynamic Strategic Network Formation (Optional/Advanced 11:57)11min
4.12: Evolution and Stochastics (Optinoal/Advanced 16:05)16min
4.13: Directed Network Formation (Optional/Advanced 16:38)16min
4.14: Application Structural Model (Optional/Advanced 35:06)35min
4.15: Week 4 Wrap4min
3 leituras
Slides from Lecture 4, with references10min
OPTIONAL - Advanced Problem Set 410min
OPTIONAL - Solutions to Advanced PS 310min
2 exercícios práticos
Quiz Week 436min
Problem Set 414min
4.8
Melhores avaliações
por 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.
por 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.
Instrutores
Matthew O. Jackson
ProfessorEconomics
Sobre 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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