Social Economic and Information Networks (70-449/73-449)
Course Materials (link)
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
- Preferential Attachment
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)
SI model (SEIN SI.nlogo)
SIR model (netlogo)
two variations on Wilensky, U. Virus Model)
Modified Erdös-Renyi (netlogo)
Modified Small-Worlds (netlogo)
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.