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


Corporate EMail 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.
Networks
Economics 2040 / Sociology 2090 / Computer Science 2850 / Information Science 2040
Cornell University, Spring 2009
MonWedFri 11:1512:05
David Easley (Economics) and
Jon Kleinberg (Computer Science)
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 online 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 third time the course is being offered;
the home pages for the
Spring 2007
and
Spring 2008
versions of the course are online as well.
See below for more information, including the
class blog,
the list of handouts,
the outline of topics,
the schedule of office hours,
and the
CMS site (which includes Cornellrestricted content).
Course Staff

Instructors:

Course Staff:
 Koralai Kirabaeva, email: kk329.
 Fang Liu, email: fl95.
 Eric Frackleton, email: epf2.
 Stuart Tettemer, email: sjt25.
 Jacob Bank, email: jeb369.
 Vaibhav Goel, email: vg57.
 Austin Lin, email: akl29.
 Tal Rusak, email: tr76.
Class Blog
List of Handouts
Outline of Topics
(1) Graph Theory and Social Networks
(2) Game Theory
(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 humansubject
experiments to study negotiation and power in networked settings.
Readings
 Chapters 911 of the Networks book draft.
(4) Information Networks and the WorldWide 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 networkbased matching markets for
sellling advertising.
Readings
 Chapters 1214 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,
richgetricher phenomena, and the ``six degrees of separation''.
Here too, we connect the models to recent empirical studies.
(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 gametheoretic models in evolutionary biology;
and the ``six degrees of separation'' phenomenon.
Books
We will be using a preliminary draft of a book
by David Easley and Jon Kleinberg,
which we developed while teaching this course over the past two years.
It is available at the Campus Store.
There are also two optional 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. Nor
ton and Company, 2006.
These two optional books contain material that supplements
and expands on some of the course topics.
Office Hours
 Mon 10  11: Austin Lin, 301 College Avenue (Information Science).
 Mon 1:15  2:15: Eric Frackleton, 328C Upson.
 Tue 1:00  2:00: Jon Kleinberg, 5134 Upson.
 Tue 3:00  4:00: Fang Liu, 328C Upson.
 Tue 4:30  5:30: Jacob Bank, 328C Upson.
 Wed 12:30  1:30: Stuart Tettemer, 328C Upson.
 Wed 2:00  4:00: David Easley, 450 Uris.
 Thu 1:00  2:00: Jon Kleinberg, 5134 Upson.
 Thu 2:30  3:30: Vaibhav Goel, 328C Upson.
 Thu 3:30  4:30: Tal Rusak, 328C Upson.
 Fri 10:00  11:00: Koralai Kirabaeva, 449A Uris.
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 (46 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 the
guidelines maintained by Cornell on this topic.
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.