Course Number
Econ 1034 and CS 134
Time and Location
Mondays and Wednesdays, 2:30–4:00 pm (Harvard time: class begins 7 minutes after start time)
Maxwell Dworkin (33 Oxford St.), G115
Ben Golub, Assistant Professor of Economics:
Office Hours: Mondays 4 – 5pm, Littauer Center, room 308.
Yaron Singer, Assistant Professor of Computer Science:
Office Hours: Wednesdays 4 – 5pm, Maxwell Dworkin, room 239.
Eric Balkanski:
OH: Wednesdays 6 – 7pm, Maxwell Dworkin, 1st floor lounge.
Eduard Talamas:
OH: Mondays 6 – 7pm, Littauer Center, basement lounge.
Gleb Romanyuk:
OH: Tuesdays 5 – 6pm, Littauer Center, basement lounge.
Alex Wang:
OH: Sundays 4 – 5pm, Lowell House, dining hall.
Anson Kahng:
OH: Thursdays 8:30 – 9:30pm, Lyman 330 (after Codelabs).
Section Times

  • Wednesday, 5-6pm, Cruft Lab 309
  • Thursday, 1-2pm, Pierce Hall 100F
  • Thursday, 5-6pm, Maxwell Dworkin 221
  • Friday, 1-2pm, Cruft Lab 309

Course Description
Networks—of social relationships, economic interdependencies, and digital interactions—are critical in shaping our lives. This course introduces models and algorithms that help us understand networks. Fundamental concepts from applied mathematics, microeconomics, and computer science will be presented through the lens of network science, in order to equip students to usefully analyze the “big data” generated by online networks. Applications discussed include the viral spread of ideas, maximizing influence, and the contagion of economic downturns. Concepts and tools covered include game theory, graph theory, data mining, and machine learning.
Requirements Fulfilled/Credit
This course counts for concentration credit in Applied Math and as an upper-level elective in Computer Science. For Economics concentrators, it counts as one of the three electives required. This course offers an optional writing requirement which, if completed, will satisfy the Economics concentration writing requirement. For Statistics concentrators, it counts as a related fields course.
Learning Objectives
At a high level, our goal is for students to be able to use the power of network science when they are confronted with network phenomena as researchers, members of teams in industry, and as policymakers. This may take the form of running their own analyses and algorithms (assuming sufficient technical background), but in any case, students who successfully complete the course will be able to communicate and collaborate productively with network scientists.
In more detail, students will walk away with three sorts of things, which are detailed on the Learning Objectives page.

  • Vocabulary, central concepts, and basic facts about networks from mathematics, computer science, economics, and sociology.
  • Between five and ten great ideas of networks that can be told in a short story: the friendship paradox, network centrality, why social networks feel small and are easy to navigate.
  • An ability to write simple and powerful models and algorithms to estimate how far a viral marketing campaign is likely to go, or how much bias there will be in a social survey.

To enjoy and succeed in this course, you will need to be comfortable with some basic math and programming, as well as with some essential ideas from economics. We will make available self-quizzes to help you gauge where you stand.

  • Math: calculus at the level of Math 1. Basic probability at the level of a good high school class (definitions and basic properties of distributions, expectation, variance).
  • Programming: CS50 taken previously or concurrently, or you should have equivalent programming ability. Programming assignments will be part of the homework, and you will be expected to get help on basic coding outside the class if you need it.
  • Economics: optimal choice in the presence of randomness; notion of a utility function; definition and basic use of expected utility.

David Easley and Jon Kleinberg, Networks, Crowds and Markets. Cambridge University Press, 2010. There is a full-text version available online.
Matthew O. Jackson Social and Economic Networks. Princeton University Press, 2008.
The grading for the course consists of three differently-weighted pieces:

  • 50% weekly problem sets (sometimes replaced with writing assignments): out Wednesday, due the next Wednesday. Lowest score dropped. This component includes the writing assignment.
  • 20% first midterm (October 14);
  • 30% second midterm (December 2).

Course Outline
Unit I. Introduction to Graph Theory (E&K 2, 18, 20 and Jackson 2, 4.2, 4.3)

  1. Graph definitions and basic properties.
  2. Essential algorithms for working with graphs.
  3. The friendship paradox.
  4. Random graphs, small-world networks, expanders.
  5. Navigating networks
  6. Power-law networks and rich-get-richer processes.

Unit II. Introduction to Game Theory (E&K 6, 17, 21 and Jackson 9.1-9.4)

  1. Definition of games and strategies in them.
  2. Solving games: elimination of dominated strategies and Nash equilibrium.
  3. Network games: agents making choices under social influence; equilibria of those games; equilibrium adoption of a new practice or product.
  4. Epidemics and the diffusion of a product.

Unit III. The Structure of Social and Economic Networks (E&K 3, 4, 13, 14 and Jackson 2.2)

  1. The strength of weak ties.
  2. Community structure and community detection; introduction to basic machine learning.
  3. Homophily (birds of a feather flock together).
  4. The structure of the web graph.
  5. Centrality measures: who is important in a network? PageRank; hubs and authorities on the web.
  6. Statistical inference of graph structure.

Unit IV. Processes on Networks: Dynamics of Behavior and Information (E&K 16, 19 and Jackson 7, 8)

  1. More on diffusion processes.
  2. Modeling the dynamics of social influence and aggregating beliefs: discrete (voter) and continuous (DeGroot).
  3. Bayesian learning, herding and information cascades.
  4. Schelling’s model of the emergence of segregation.

Unit V. Networked Markets and Other Applications (E&K 8, 10, 22)

  1. Introduction to matching markets: labor markets and kidney exchanges.
  2. Selfish routing in transportation networks, the price of anarchy, and Braess’s paradox.
  3. Knowledge graphs (Wikipedia).

Collaboration and Academic Integrity

  • Students are encouraged to discuss the problem sets with each other.
  • Each student must indicate on his or her solution the names of all students with whom he or she worked on the problems.
  • Each student’s homework solutions must reflect his or her own understanding of the problem and not be copied in substance (i.e., with or without changes of phrasing).
  • On all written assignments, Harvard’s standard guidelines regarding plagiarism apply. They are relevant to the problem sets and especially the extended writing assignment. The highlights:
    • You may not submit the same work to two different courses without permission.
    • If you use something, cite it.
    • Beware of the extended paraphrase, which is the easiest kind of plagiarism to commit accidentally. If you are reasonably close to quoting something, it’s better to just quote it exactly. (A block quote is a convenient typographical device for this.) You are committing plagiarism if you take a passage and copy it out, changing the phrasing and some of the order but keeping much of the substance and flow of ideas. The guidelines have good examples. If you must paraphrase, explicitly say (for example, in a footnote) that you are writing a close paraphrase of someone else’s passage, and say where the paraphrase begins and ends. A standard parenthetical citation will not suffice.

Accommodations for Students with Disabilities
Students needing academic adjustments or accommodations because of a documented disability must contact one of the course heads (Prof. Golub or Prof. Singer) by the end of the second week of the term, September 11, and present their Faculty Letter from the Accessible Education Office (AEO). Failure to do so may result in the Course Head’s inability to respond in a timely manner. All discussions will remain confidential.