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
Instructor: Matthew O. Jackson
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(743 reviews)
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There are 8 modules in this course
Examples of Social Networks and their Impact, Definitions, Measures and Properties: Degrees, Diameters, Small Worlds, Weak and Strong Ties, Degree Distributions
What's included
12 videos3 readings3 assignments
Homophily, Dynamics, Centrality Measures: Degree, Betweenness, Closeness, Eigenvector, and Katz-Bonacich. Erdos and Renyi Random Networks: Thresholds and Phase Transitions
What's included
11 videos3 readings3 assignments
Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation.
What's included
12 videos3 readings4 assignments
Game Theoretic Modeling of Network Formation, The Connections Model, The Conflict between Incentives and Efficiency, Dynamics, Directed Networks, Hybrid Models of Choice and Chance.
What's included
15 videos3 readings2 assignments
Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data.
What's included
12 videos3 readings3 assignments
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..
What's included
9 videos3 readings2 assignments
Network Games, Peer Influences: Strategic Complements and Substitutes, the Relation between Network Structure and Behavior, A Linear Quadratic Game, Repeated Interactions and Network Structures.
What's included
10 videos4 readings2 assignments
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Reviewed on Jun 5, 2020
Interesting survey of modern network theory, from Erdos-Renyi random graphs, to SIS ("flu") models, and games on networks. Rather academic at times, without the rigour.
Reviewed on Sep 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.
Reviewed on May 16, 2020
Matt is a fantastic instructor and has inspired many new ideas for my PhD project. Putting this course up here for free is extremely generous of both Matt and Coursera.
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