Social Economic and Information Networks (70-449/73-449)
Interaction is a fundamental part of social science: firms market products to consumers, people share opinions and information with their friends, workers collaborate on projects, agents form alliances and coalitions. In this course, we will use the emerging field of social networks to put structure on this diverse mass of connections.
Using a mixture of theoretical, empirical, and computational methods, we will learn about the structure and function of social networks. We will look at how an individual's position in a social network reflects her role in the community. We will learn to identify the most important individuals in a social network. We will consider how our own position in the social network affects our behavior, opinions, and outcomes. And we will explore where social networks come from, and what affects their structure. The material in this course will be interdisciplinary, drawn from the fields of math, computer science, physics, sociology, political science, and economics.
By the end of the course, you will have the tools and knowledge needed to analyze social network data on your own. There are two projects that will provide you an opportunity to use your skills in a particular context. In the midsemester data project, you will use data collected from the class. The final project will allow you to use whatever data you wish to answer your own questions about the world.
Networks, Crowds, and Markets, by David Easley and Jon Kleinberg
Available for purchase on Amazon, or free online (here)
The Structure and Function of Complex Networks, by Mark Newman (link)
Required Software (both free and platform-independent):
Other Useful Texts:
Networks: An Introduction, by M.E.J. Newman
Six Degrees: The Science of a Connected Age, by Duncan J. Watts
Social and Economic Networks, by Matthew O. Jackson
Network Science: Theory and Applications, by Ted G. Lewis
Social Network Analysis, by John Scott
Part 1: Introduction
Why study networks?
Famous Networks of History
Data Collection, Representation
Part 2: Network Measures
Network Taxonomy: Weighted and Directed Networks, Bipartite Networks
Basic Measures: Degree, Paths, Distance, Degree Distribution, Clustering
Centrality Measures: Closeness, Betweenness, Eigenvector, Centralization, Directed Centrality
Part 3: Relationships and Interactions
Relationships: Reciprocity, Triadic Closure, Homophily and Assortativity
Bridging Gaps: Closure, Brokerage, Embeddedness, Social Captial
Community Structure and Community Detection
Part 4: Models of Social and Information Networks
Watts-Strogatz Small World
Part 5: Diffusion
Epidemics: SI/SIR models, Diffusion on a Network, Control of Disease
Behavior: Peer Effects, Network Effects
Information: Learning, Social Influence, Opinion Formation, Technology, Simple/Complex Contagions
Control of Information Diffusion: Injection Points, Influencers
Network Resilience and Failure
(See below for details on attribution)
Netlogo is a great platform for creating and distributing simulations. Here, I’ve put together some programs to illustrate basic network models. At the moment, I leave the code untouched and use them as a demo.
You could also have students modify the code as part of an exploratory exercise. I haven’t done this yet, but I think it could be really cool—if you do something like that in your class, please let me know what you come up with!
Final Data Project (pdf)
These slides, netlogo programs, and other materials are are available to the community under a Creative Commons Attribution-NonCommercial 4.0 International License (In short: you may use them, so long as you don’t profit from them and attribute them appropriately) Attribution should include my name, and an active link to the relevant page.
I try to cite source for all of the images and examples I use that are not mine. If you use one of those, please cite don’t cite me: cite the original source! If you find something that has been mis-attributed, please contact me.