《Social and Economic Networks: Models and Analysis》课堂笔记

About this Course

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

1. Introduction, Empirical Background and Definitions

• 1.1 Introduction

• 1.2 Examples and Chanllenges

• 1.3 Background Definitions and Notation

• 1.4 Definitions and Notation

• 1.5 Diameter

• 1.6 Diameter and Trees

• 1.7 Diameters of Random Graphs

• 1.8 Diameters in the World

• 1.9 Degree Distributions

• 1.10 Clustering

2. Background, Definitions, and Measures Continued

• 2.1 Homophily

• 2.2 Dynamics and Tie Strength

• 2.3 Centrality Measures- Degree, Closeness, Decay and Betweenness

• 2.4 Centrality - Eigenvector Measures

• 2.5a Application - Centrality Measures

• 2.5b Application - A new centrality measure__Diffusion Centrality

• 2.6 Random Networks

• 2.7 Random Networks - Thresholds and Phase Transitions

• 2.8 A Threshold Theorem (Optional)

• 2.9 A Small World Model

3. Random Networks

• 3.1 Growing Random Networks

• 3.2 Mean Field Approximations

• 3.3 Preferential Attachment

• 3.4 Hybrid Models

• 3.5 Fitting Hybrid Models

• 3.6 Block Models

• 3.7 ERGMs

• 3.8 Estimating ERGMs

• 3.9 Statistical Exponential Random Graph Model(SERGMs)

• 3.10 SUGMs

• 3.11 Estimating SUGMs

4. Strategic Network Formation

• 4.1 Strategic Network Formation

• 4.2 Pairwise Stability and Efficiency

• 4.3 Connections Model

• 4.4 Efficiency in Connections Model

• 4.5 Pair Stability in the Connections Model

• 4.6 Externalities and the Coauthor Model

• 4.7 Network Formation and Transfers

• 4.8 Heterogeneity in Strategic Models

• 4.9 SUGMs and Strategic Network Formation

• 4.10 Pairwise Nash Stability

• 4.11 Dynamic Strategic Network Formation

• 4.12 Evolution and Stochastics

• 4.13 Directed Network Formation

• 4.14 Application Structural Model

5. Diffusion on Networks

• 5.1 Diffusion

• 5.2 Bass Model

• 5.3 Diffusion on Random Networks

• 5.4 Giant Component Poisson Case

• 5.5 Susceptible Infected Susceptible (SIS) Model

• 5.6 Solving the SIS Model

• 5.7 Solving the SIS Model - Ordering

• 5.8 Fitting a Diffusion Model to Data

• 5.9 Application - Financial Contagions

• 5.10 Application- Financial Contagions Simulations

6. Learning on Networks

• 6.1 Learning

• 6.2 DeGroot Model

• 6.3 Convergence in DeGroot Model

• 6.4 Proof of Convergence Theorem

• 6.5 Influence

• 6.6 Examples of Influence

• 6.7 Information Aggregation

• 6.8 Learning Summary

7. Games on Networks

• 7.1 Games on Networks

• 7.2 Complements and Substitutes

• 7.3 Properties of Equilibria

• 7.4 Multiple Equilibria

• 7.5 An Application

• 7.6 Beyond 0-1 Choices

• 7.7 A Linear-Quadratic Model

• 7.8 Repeated Games and Networks - Favor Exchange

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