High School Dating
(Bearman, Moody, and Stovel, 2004)
(Image by Mark Newman)
Corporate E-Mail Communication
(Adamic and Adar, 2005)
Economics 2040 / Sociology 2090 / Computer Science 2850 / Information Science 2040
Cornell University, Fall 2017
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.
- Amy Zhong asz35
- Austin Liu arl96
- Brandon Quinlan bmq4
- Eric Wu ew373
- Evan Kravitz esk95
- Fikri Pitsuwan fp67
- Heesun Jun hj369
- HweeLin Yao hy384
- Jacqueline Law jjl284
- James Cramer jcc393
- Jessica Prague jnp49
- Jialu Bao jb965
- Kate Brind kbb55
- Ke Zhang kz238
- Marta Maksymuk mm2556
- Miles Wittenberg maw367
- Pravir Samtani ps795
- Saakshi Singhal ss992
- Samantha Dimmer sed87
- Vincent Sun vhs7
- Yutong Wu yw644
CMS site, you can log in with your Cornell NetID to find information
about your course grades and also to upload solutions to homework.
Solutions to all problem sets
must be submitted through the CMS site.
The site will require files to be in PDF format.
Also, you should check the CMS site at the start of the semester
to make sure that you are able to log in.
Please let us know if you experience any difficulties with this.
Class Discussion on Piazza
We will be using Piazza as a discussion forum for the class. Feel
free to post any questions you have about class. Only students and
instructors in the class will be allowed to post to it, and students
and instructors and TAs can all answer questions.
To get started,
please add yourself to the
class piazza page
as a student using
your @cornell.edu email address.
- Mon 5:00 - 6:00: Yutong and James, G11 Gates
- Tue 1:30 - 2:30: Prof. Easley, 227 Gates
- Tue 3:00 - 4:00: Milo and Brandon, G11 Gates
- Wed 1:00 - 2:00: Jackie, G11 Gates
- Wed 3:00 - 4:00: Prof. Kleinberg, 318 Gates
- Wed 4:00 - 5:00: Pravir and Amy, G11 Gates
- Wed 5:00 - 6:00: Heesun and Ke, G11 Gates
- Wed 6:00 - 7:00: Samantha and Austin, G11 Gates
- Thu 1:00 - 2:00: Evan and Eric, G19 Gates
- Thu 3:00 - 4:00: Jessica and Jialu, G11 Gates
- Thu 4:00 - 5:00: Marta and Hwee Lin, G11 Gates
- Thu 5:00 - 6:00: Kate and Saakshi, G11 Gates
- Thu 6:00 - 7:00: Fikri and Vincent, G11 Gates
- We will be using the book Networks, Crowds, and Markets
(Cambridge University Press, 2010), which we
wrote while teaching this course over the past several
years. A complete draft is on-line at the
Web page for the book,
and the hardcopy version is for sale at the Campus Store.
- Over the past several years, we've offered an
on-line version of the course
through the edX platform. The materials from this course are now archived,
and you can access them by registering on-line at the edX site. Although you
can no longer take the edX course as a student,
by registering you get access to the videos and on-line exercises.
In the course this fall, this edX material will serve as a set of optional
resources that you may find helpful for alternate presentations as
well as a source of additional practice exercises.
We'll use clickers in lecture.
You must use a clicker from the i-Clicker system.
Academic integrity guidelines require that you may only
use your own registered clicker during class.
The process for registering an i-Clicker is described in
the registration instructions.
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 human-subject
experiments to study negotiation and power in networked settings.
(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
(5) Network Dynamics: Population Models
Networks are powerful conduits for the flow of
information, opinions, beliefs, innovations, and technologies.
We begin by considering how these processes operate at
the level of populations, when we can't necessarily observe
the network itself, but only its effects on aggregate behavior.
As part of this, we consider phenomena including information
cascades, "tipping points" in the success of products with network effects,
and the distribution of popularity.
- Chapter 7, Chapters 16-18, and Chapter 22.
(6) Network Dynamics: Structural Models
(7) Institutions and Aggregate Behavior
Finally, a perspective based on networks can provide novel insights
into the structure of social institutions, and
into basic policy questions in many areas.
We illustrate this theme with examples based on markets, voting theory,
and property rights.
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.
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.
- Midterm: Wednesday, October 4, in class.
- Final exam: Wednesday, December 13, at 9:00 AM.
- 9 problem sets. As described above,
these must be submitted using the
CMS site, by the start of class on the days they are due.
- Class blog: There is a class weblog, and each student
should make three posts to it as part of the graded coursework,
following the details described in the accompanying
Grades on the homework, blog posts, midterm,
and final will be weighted as follows:
- Homework: 36%
- Midterm: 20%
- Final Exam: 35%
- Blog Posts: 9%
- iClicker participation: 0% (may have a tiny effect in borderline cases)
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