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 2018
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.
**Room change on November 2** The November 2 lecture will be held in Call Auditorium.
- Alan Yan ayy23
- Alisha Mithal am2658
- Brandon Quinlan bmq4
- Eric Wu ew373
- Fikri Pitsuwan fp67
- Heather Zhu yz927
- Heesun Jun hj369
- Hyun Seo (Lucy) Lee hl566
- Jessie Yee jy393
- Jia Wei jw984
- Kate Brind kbb55
- Ke Zhang kz238
- Laasya Renganathan lpr46
- Marta Maksymuk mm2556
- Peter Zeiger pjz27
- Ritvik Taneja rt328
- Seongheon Yoon sy356
- Stephanie Chang sc2524
- Tongtong Lian tl547
- Tyler Wang tw325
- Valiko Ergemlidze vae4
- Yun Ping Tseng yt376
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 typed and 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: Lucy and Alan, G11 Gates
- Tue 1:30 - 2:30: Prof. Easley, 227 Gates
- Tue 3:00 - 4:00: Daniel and Ke, G11 Gates
- Wed 9:00 - 10:00: Prof. Benson, 203 Gates
- Wed 1:00 - 2:00: Yun Ping and Brandon, G11 Gates
- Wed 4:00 - 5:00: Jessie and Tongtong, G11 Gates
- Wed 5:00 - 6:00: Heather and Valiko, G11 Gates
- Wed 6:00 - 7:00: Jia and Stephanie, G11 Gates
- Thu 1:00 - 2:00: Eric and Laasya, G11 Gates
- Thu 3:00 - 4:00: Heesun and Peter, G11 Gates
- Thu 4:00 - 5:00: Alisha and Fikri, G11 Gates
- Thu 5:00 - 6:00: Kate and Ritvik, G11 Gates
- Thu 6:00 - 7:00: Tyler and Marta, G11 Gates
- We will be using the book Networks, Crowds, and Markets
(Cambridge University Press, 2010), which Professor Easley
co-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
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 3, in class.
- Final exam: Sunday, December 9, at 9:00 AM.
- 9 problem sets. As described above,
these must be typed and submitted ad PDFs 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
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