Social and Economic Networks: Models and Analysis

Universidad de Stanford

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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

Resultados profesionales del estudiante

20%

comenzó una nueva carrera después de completar estos cursos

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

Aprox. 30 horas para completar

Inglés (English)

Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)

Habilidades que obtendrás

Social NetworkGame TheoryNetwork AnalysisNetwork Theory

Resultados profesionales del estudiante

20%

comenzó una nueva carrera después de completar estos cursos

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

Aprox. 30 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4.87/5 (42 calificaciones)

Matthew O. Jackson

ofrecido por

Universidad de Stanford

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.

Programa - Qué aprenderás en este curso

Calificación del contenido96%(3,226 calificaciones)

Semana

Semana 1

4 horas para completar

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

4 horas para completar

12 videos (Total 118 minutos), 3 lecturas, 3 cuestionarios

12 videos

An Introduction to the Course4m

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 lecturas

Syllabus10m

Slides from Lecture 1, with References10m

OPTIONAL - Advanced Problem Set 110m

3 ejercicios de práctica

Quiz Week 130m

Problem Set 130m

Optional: Empirical Analysis of Network Data using Gephi or Pajek30m

Semana

Semana 2

4 horas para completar

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

4 horas para completar

11 videos (Total 105 minutos), 3 lecturas, 3 cuestionarios

11 videos

2.1: Homophily10m

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 lecturas

Slides from Lecture 2, with references10m

OPTIONAL - Advanced Problem Set 210m

OPTIONAL - Solutions to Advanced PS 110m

3 ejercicios de práctica

Quiz Week 230m

Problem Set 230m

Optional: Empirical Analysis of Network Data30m

Semana

Semana 3

5 horas para completar

Random Networks

Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation.

5 horas para completar

12 videos (Total 143 minutos), 3 lecturas, 4 cuestionarios

12 videos

3.1: Growing Random Networks17m

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 lecturas

Slides from Lecture 3, with references10m

OPTIONAL - Advanced Problem Set 310m

OPTIONAL - Solutions to Advanced PS 210m

4 ejercicios de práctica

Quiz Week 330m

Problem Set 330m

Optional: Empirical Analysis of Network Data30m

Optional: Using Statnet in R to Estimate an ERGM30m

Semana

Semana 4

5 horas para completar

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 horas para completar

15 videos (Total 209 minutos), 3 lecturas, 2 cuestionarios

15 videos

4.1: Strategic Network Formation12m

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 lecturas

Slides from Lecture 4, with references10m

OPTIONAL - Advanced Problem Set 410m

OPTIONAL - Solutions to Advanced PS 310m

2 ejercicios de práctica

Quiz Week 430m

Problem Set 430m

Semana

Semana 5

5 horas para completar

Diffusion on Networks

Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data.

5 horas para completar

12 videos (Total 158 minutos), 3 lecturas, 3 cuestionarios

12 videos

5.1: Diffusion12m

5.2: Bass Model12m

5.3: Diffusion on Random Networks 9m

5.4: Giant Component Poisson Case 15m

5.5: SIS Model17m

5.6: Solving the SIS Model 9m

5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16)24m

5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47)22m

5.8b: Application: Financial Contagions (Optional/Advanced 12:47)12m

5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41)13m

5.9: Diffusion Summary 3m

5.10: Week 5 Wrap4m

3 lecturas

OPTIONAL - Advanced Problem Set 510m

OPTIONAL - Solutions to Advanced PS 410m

Slides from Lecture 5, with references10m

3 ejercicios de práctica

Quiz Week 530m

Problem Set 530m

Optional: Empirical Analysis of Network Data30m

Semana

Semana 6

3 horas para completar

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..

3 horas para completar

9 videos (Total 100 minutos), 3 lecturas, 2 cuestionarios

9 videos

6.1: Learning26m

6.2: DeGroot Model 15m

6.3: Convergence in DeGroot Model 13m

6.4: Proof of Convergence Theorem (Optional/Advanced 10:25)10m

6.5: Influence 6m

6.6: Examples of Influence 8m

6.7: Information Aggregation 9m

6.8: Learning Summary 4m

6.9: Week 6 Wrap4m

3 lecturas

Slides from Lecture 6, with references10m

OPTIONAL - Advanced Problem Set 610m

OPTIONAL - Solutions to Advanced PS 510m

2 ejercicios de práctica

Quiz Week 630m

Problem Set 630m

Semana

Semana 7

4 horas para completar

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.

4 horas para completar

10 videos (Total 141 minutos), 4 lecturas, 2 cuestionarios

10 videos

7.1: Games on Networks12m

7.2: Complements and Substitutes 19m

7.3: Properties of Equilibria 14m

7.4: Multiple Equilibria 13m

7.5: An Application 7m

7.6: Beyond 0-1 Choices 20m

7.7: A Linear Quadratic Model 14m

7.8: RepeatedGames and Networks 24m

7.9: Week 7 Wrap 4m

7.9b: Course Wrap10m

4 lecturas

Slides from Lecture 7, with references10m

OPTIONAL - Advanced Problem Set 710m

OPTIONAL - Solutions to Advanced PS 610m

OPTIONAL - Solutions to Advanced PS 710m

2 ejercicios de práctica

Quiz Week 730m

Problem Set 730m

Semana

Semana 8

1 hora para completar

Final Exam

The description goes here

1 hora para completar

1 ejercicio de práctica

Final30m

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Social and Economic Networks: Models and Analysis