Social and Economic Networks: Models and Analysis
Stanford University
课程信息
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
Syllabus - What you will learn from this course
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 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
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 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
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 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
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 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
5
Section
Diffusion on Networks
Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data.
12 videos (Total 158 min), 3 readings, 3 quizzes
12 videos
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 readings
OPTIONAL - Advanced Problem Set 510m
OPTIONAL - Solutions to Advanced PS 410m
Slides from Lecture 5, with references10m
3 practice exercises
Quiz Week 518m
Problem Set 512m
Optional: Empirical Analysis of Network Data4m
6
Section
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..
9 videos (Total 100 min), 3 readings, 2 quizzes
9 videos
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 readings
Slides from Lecture 6, with references10m
OPTIONAL - Advanced Problem Set 610m
OPTIONAL - Solutions to Advanced PS 510m
2 practice exercises
Quiz Week 614m
Problem Set 612m
7
Section
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.
10 videos (Total 141 min), 4 readings, 2 quizzes
10 videos
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 readings
Slides from Lecture 7, with references10m
OPTIONAL - Advanced Problem Set 710m
OPTIONAL - Solutions to Advanced PS 610m
OPTIONAL - Solutions to Advanced PS 710m
2 practice exercises
Quiz Week 724m
Problem Set 716m
8
Section
Final Exam
The description goes here
1 quiz
1 practice exercises
Final14m
4.8
Top Reviews
By MR•Nov 2nd 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 :)
By 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.
Instructor
Matthew O. Jackson
ProfessorAbout 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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