High School Dating (Bearman, Moody, and Stovel, 2004) (Image by Mark Newman)

Corporate E-Mail Communication (Adamic and Adar, 2005; image by the authors)

Note: This is not the current semester's course Web page. For current course information, handouts, and homework assignments, please visit the present semester's version of the course.

A course on how the social, technological, and natural worlds are connected, and how the study of networks sheds light on these connections. Topics include: how opinions, fads, and political movements spread through society; the robustness and fragility of food webs and financial markets; and the technology, economics, and politics of Web information and on-line communities.

The course is designed at the introductory undergraduate level with no formal prerequisites; it satisfies the Arts & Sciences Social and Behavioral Analysis (SBA) distribution and the Engineering Liberal Studies (SBA group) distribution. (See also the poster announcing the course.)

This is the second time the course is being offered; the course home page from the Spring 2007 offering of the course is on-line as well.

See below for more information, including the class blog and digest blog, the list of handouts, the outline of topics, the schedule of office hours, and the CMS site (which includes Cornell-restricted content).

Course Staff

• Instructors:
  • David Easley, 450 Uris Hall.
  • Jon Kleinberg, 5134 Upson Hall.
• Course Staff:
  • Bistra Dilkina, 5151 Upson Hall, email: bistra@cs.cornell.edu.
  • Max Mihm, email: mam293.
  • Anand Bhaskar, email: ab394.
  • Ethan Feldman, email: ef54.
  • Ben Cole, email: bsc36.
  • Narie Foster, email: nsf6.
  • Scott Grabnic, email: smg63.
  • Ben Pu, email: ben.pu.

Class Blog

There is a class weblog that we will be maintaining as part of the course, and posting to the blog will be part of the graded coursework, as described in the accompanying handout. The blog is hosted at Cornell, as part of the Expert Voices Gateway of the National Science Digital Library.

Information about the mechanics of posting can be found in the opening blog post.

The course staff will also be maintaining a parallel digest blog, with short summaries and further reading related to particular posts from the class blog.

List of Handouts

• Student Information Sheet (1/21; please fill out and return).
• Guidelines for Blog Posts. (1/21)

Outline of Topics

(1) Graph Theory and Social Networks

The course begins with a discussion of some of the general properties of networks. It develops this through examples from social network analysis, including the famous ``strength of weak ties'' hypothesis in sociology, and it connects these themes to recent large-scale empirical studies of on-line social networks.

Readings

• Chapters 1-3 of the Networks book draft.
• Chapter 2 of The Tipping Point, Sections 1-5. (The remainder of the chapter is useful as well, but less central to the content of the lectures.)

Optional further reading:

• Mark Granovetter. The strength of weak ties. American Journal of Sociology, 78:1360--1380, 1973.
  (This is Granovetter's original paper describing his work covered in Chapter 2 of the Networks book.)

(2) Game Theory

Since most network studies require us to consider not only the structure of a network but also the behavior of the agents that inhabit it, a second important set of techniques comes from game theory. This too is introduced in the context of examples, including the design of auctions and some ``paradoxical'' phenomena surrounding network traffic congestion.

Readings

• Chapters 4 and 6 of the Networks book draft. (We will cover Chapter 5 later in the semester.)
• Chapter 1 of Micromotives and Macrobehavior.

Optional further reading:

• Ignacio Palacios-Huerta. Professionals Play Minimax. Review of Economic Studies, 70(2003), pp. 395-415,
  (This paper is discussed in Example 4 of Chapter 4.)

(3) Markets and Strategic Interaction on Networks

The interactions among participants in a market can naturally be viewed as a phenomenon taking place in a network, and in fact network models provide valuable insights into how an individual's position in the network structure can translate into economic outcomes. This provides a natural illustration of how graph theory and game theory can come together in the development of models for network behavior. Our discussion in this part of the course also builds on a large body of sociological work using human-subject experiments to study negotiation and power in networked settings.

Readings

• Chapters 7-9 of the Networks book draft.

(4) Information Networks and the World-Wide Web

The Internet and the Web of course are central to the argument that computing and information is becoming increasingly networked. Building on the earlier course topics, we describe why it is useful to model the Web as a network, discussing how search engines make use of link information for ranking, how they use ideas related to power and centrality in social networks, and how they have implemented network-based matching markets for sellling advertising.

Readings

• Chapters 10-11 of the Networks book draft.

(5) Network Dynamics and Cascading Behavior

Networks are powerful conduits for the flow of information, opinions, beliefs, innovations, and technologies. We discuss how models of interaction can give us ways of reasoning about processes that cascade through networks, as well as related problems such as the distribution of popularity and rich-get-richer phenomena. Here too, we connect the models to recent empirical studies.

Readings

• Chapters 12-14 and 16 of the Networks book draft.
• Introduction and Chapters 1 and 6 of The Tipping Point.
• Chapter 3 of Micromotives and Macrobehavior.

Optional further reading:

• Michael Suk-Young Chwe. Structure and Strategy in Collective Action. American Journal of Sociology 105(1999), pp. 127--156.

(6) Policy Considerations and Further Applications

A perspective based on networks can provide novel insights into basic questions in many other areas as well. In particular, we use this perspective to consider policy questions based on voting theory, statistical discrimination, and intellectual property. We also consider the use of network feedback effects in capturing the role information and quality assurance in the robustness of markets; the use of game-theoretic models in evolutionary biology; and the ``six degrees of separation'' phenomenon.

Readings

• Chapters 5, 15, and 17 of the Networks book draft.
• Chapter 4 of Micromotives and Macrobehavior, pages 147-155.

Optional links:

• Some on-line simulations of the Schelling segregation model at MIT, Northwestern, and Iowa State.

Books

We will be using a preliminary draft of a book by David Easley and Jon Kleinberg, which we developed while teaching this course last year. It is available at the Campus Store.

There are also two other books for the course:

• The Tipping Point: How Little Things Can Make a Big Difference. Malcolm Gladwell, Little, Brown and Company, 2002.
• Micromotives and Macrobehavior. Thomas C. Schelling, W. W. Norton and Company, 2006.

The books will also be supplemented with readings that will be posted to the course Web page.

Office Hours

• Mon 1:30 - 2:30: Ben Cole, 301 College Avenue (Information Science).
• Mon 4:00 - 5:00: Bistra Dilkina, 5151 Upson.
• Tue 10:30 - 11:30: Narie Foster, 328B Upson.
• Tue 3:00 - 5:00: David Easley, 450 Uris.
• Wed 10:00 - 11:00: Scott Grabnic, 328B Upson.
• Wed 1:15 - 2:30: Jon Kleinberg, 5134 Upson.
• Wed 3:30 - 4:30: Ben Pu, 328B Upson.
• Thu 1:15 - 2:00: Jon Kleinberg, 5134 Upson.
• Thu 3:00 - 4:00: Max Mihm, 465 Uris.
• Fri 1:00 - 2:00: Ethan Feldman, 328B Upson.
• Fri 4:00 - 5:00: Anand Bhaskar, 328B Upson.

Prerequisites

Almost no knowledge of specific mathematical content is assumed, other than some basic probability (random variables, expectation, independence, and conditional probability), which we will briefly review when it first arises.

However, the main goal of the course will be to build mathematical models of the processes that take place in networks. As such, students will be expected to interpret and work with mathematical models as they come up the course; at the same time, students should also think about how to relate these models to phenomena at a qualitative level.

Coursework

• Midterm.
• Final exam.
• Approximately 6 problem sets.
• A short (4-6 page) paper due the last week of class. The paper is designed to be an exploration of a topic related to the course, containing both a discussion of prior work, and some novel discussion or analysis of the topic.
• Class blog: As discussed above, there is a class weblog and each student should make at least three posts to it as part of the graded coursework. See the accompanying handout describing the format and schedule for blog posts.

Grades on homework, the paper, blog posts, the midterm, and the final will be weighted as follows:
• Midterm: 20%
• Final: 30%
• Homework: 20%
• Short Paper: 20%
• Blog Posts: 10%

Academic Integrity

You are expected to maintain the utmost level of academic integrity in the course. Any violation of the code of academic integrity will be penalized severely.

You are allowed to collaborate on the homework to the extent of formulating ideas as a group. However, you must write up the solutions to each problem set completely on your own, and understand what you are writing. You must also list the names of everyone that you discussed the problem set with.

Collaboration is not allowed on the other parts of the coursework.

Finally, plagiarism deserves special mention here. Including text from other sources in written assignments without quoting it and providing a proper citation constitutes plagiarism, and it is a serious form of academic misconduct. This includes cases in which no full sentence has been copied from the original source, but large amounts of text have been closely paraphrased without proper attribution. To get a better sense for what is allowed, it is highly recommended that you consult pages 16-22 of the Academic Integrity document at web.cornell.edu/UniversityFaculty/docs/AI.Acknow.pdf. It is also worth noting that search engines have made plagiarism much easier to detect. This is a very serious issue; instances of plagiarism will very likely result in failing the course.