4.8

306 ratings

•

64 reviews

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

Section

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

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

Syllabus10m

Slides from Lecture 1, with References10m

OPTIONAL - Advanced Problem Set 110m

Quiz Week 128m

Problem Set 112m

Optional: Empirical Analysis of Network Data using Gephi or Pajek8m

Section

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

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

Slides from Lecture 2, with references10m

OPTIONAL - Advanced Problem Set 210m

OPTIONAL - Solutions to Advanced PS 110m

Quiz Week 216m

Problem Set 210m

Optional: Empirical Analysis of Network Data6m

Section

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

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

Slides from Lecture 3, with references10m

OPTIONAL - Advanced Problem Set 310m

OPTIONAL - Solutions to Advanced PS 210m

Quiz Week 326m

Problem Set 36m

Optional: Empirical Analysis of Network Data4m

Optional: Using Statnet in R to Estimate an ERGM6m

Section

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

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

Slides from Lecture 4, with references10m

OPTIONAL - Advanced Problem Set 410m

OPTIONAL - Solutions to Advanced PS 310m

Quiz Week 436m

Problem Set 414m

Section

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

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

OPTIONAL - Advanced Problem Set 510m

OPTIONAL - Solutions to Advanced PS 410m

Slides from Lecture 5, with references10m

Quiz Week 518m

Problem Set 512m

Optional: Empirical Analysis of Network Data4m

Section

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

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

Slides from Lecture 6, with references10m

OPTIONAL - Advanced Problem Set 610m

OPTIONAL - Solutions to Advanced PS 510m

Quiz Week 614m

Problem Set 612m

Section

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

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

Slides from Lecture 7, with references10m

OPTIONAL - Advanced Problem Set 710m

OPTIONAL - Solutions to Advanced PS 610m

OPTIONAL - Solutions to Advanced PS 710m

Quiz Week 724m

Problem Set 716m

Section

The description goes here...

1 quiz

Final14m

4.8

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.

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