If you are taking or shopping the class, please take a look at the new announcements posted September 4—we are in a new room (Maxwell Dworkin G115) and there are several updates about the structure of the course.
How do rumors spread? Will a new social app take off? How does Google analyze links to decide which web pages are important?
This term we are teaching a new course about networks, addressing these questions and many others. The course is offered in economics and computer science, and interested students from any discipline are encouraged to enroll. We will cover tools and concepts from game theory, graph theory, data mining, and machine learning.
A preliminary syllabus is available. If you’d like some particular examples of what you’ll learn and do, see our Learning Objectives. For a richer sense of what the course will feel like, you can browse a version of the main textbook online. Chapter 1 is a sweeping overview of the big topics and ideas. For some of the specific subjects we’ll be covering, and a sense of the level of detail, we recommend:

  • Chapter 17 on markets and behaviors with network effects: should I buy this phone? Should I go out and join this protest?
  • Chapter 19 on the “small-world” character of social networks: why do you have a friend of a friend in almost every country and almost every industry?
  • Chapter 4 on describing and measuring structure in social networks—for example, homophily (“birds of a feather flock together”).

We will generally work through formal models in somewhat more detail than this text, but will also keep the background required minimal; see the syllabus for the prerequisites we can’t do without.
The course is led by Ben Golub (Economics) and Yaron Singer (Computer Science), assisted by a staff of wonderful teaching fellows from both departments.

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