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.
Social and Economic Networks: Models and Analysis
ofrecido por
Social and Economic Networks: Models and Analysis
Universidad de Stanford
Acerca de este Curso
50,090 vistas recientes
Resultados profesionales del estudiante
25%
comenzó una nueva carrera después de completar estos cursos
Habilidades que obtendrás
Social NetworkGame TheoryNetwork AnalysisNetwork Theory
Resultados profesionales del estudiante
25%
comenzó una nueva carrera después de completar estos cursos
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. 25 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Programa - Qué aprenderás en este curso
3 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
3 horas para completar
12 videos (Total 118 minutos), 3 lecturas, 3 cuestionarios
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 lecturas
Syllabus10m
Slides from Lecture 1, with References10m
OPTIONAL - Advanced Problem Set 110m
3 ejercicios de práctica
Quiz Week 128m
Problem Set 112m
Optional: Empirical Analysis of Network Data using Gephi or Pajek8m
3 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
3 horas para completar
11 videos (Total 105 minutos), 3 lecturas, 3 cuestionarios
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 lecturas
Slides from Lecture 2, with references10m
OPTIONAL - Advanced Problem Set 210m
OPTIONAL - Solutions to Advanced PS 110m
3 ejercicios de práctica
Quiz Week 216m
Problem Set 210m
Optional: Empirical Analysis of Network Data6m
4 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.
4 horas para completar
12 videos (Total 143 minutos), 3 lecturas, 4 cuestionarios
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 lecturas
Slides from Lecture 3, with references10m
OPTIONAL - Advanced Problem Set 310m
OPTIONAL - Solutions to Advanced PS 210m
4 ejercicios de práctica
Quiz Week 326m
Problem Set 36m
Optional: Empirical Analysis of Network Data4m
Optional: Using Statnet in R to Estimate an ERGM6m
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
4.2: 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 436m
Problem Set 414m
Revisiones
Principales revisiones sobre SOCIAL AND ECONOMIC NETWORKS: MODELS AND ANALYSIS
por MRNov 1, 2017
Really enjoyed this course. The professor is really good and covers quite a lot of ground during the lectures. Good way to get into complex networks! Probably gonna do some studying on my own now :)
por SWAug 8, 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 JKDec 9, 2018
Excellent course. The labs are the best. Pajek and Gephi will be handy for network graphing and analyzing data. Thank you Professor Matthew Jackson. Your work is very good for reference.
por MGApr 16, 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.
por TCApr 29, 2017
An overview of concepts and models of how networks form. There are applicable with basic concepts from probability theory, statistics, and some light calculus astonishingly well.
por HTDec 21, 2016
The course is vast. The Professor is to the point and doesn't lack knowledge in his field.\n\nI'd recommend this course for anyone interested in Economics. Loved it.
por ACSep 3, 2016
The course is a helpful first step in the field of network science. Presenting clearly many complex ideas that are important for understanding current research.
por OJJul 6, 2018
An excellent walkthrough of the literature. I just wish there were more empirical exercises and and more hands-on work with the data and algorithms.
por GPJun 24, 2016
A very usefull course very different from others. Although for coursera standars, its very extensive and not so basic, that´s why I think its great
por EAMay 14, 2017
Fantastic course! Compelling examples of application and an enthusiasm for the concepts that is contagious. Would love more from Prof. Jackson!
por CTFeb 2, 2020
Professor Jackson is very clear and insightful! Loved his style of teaching. I hope more Economics professors were such good teachers as him.
por PVJun 20, 2017
An excellent and very useful course. Recommend with no reserves to anyone willing to understand the world from a well structured perspective
Acerca de 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.
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