CS395T: Spectral Graph Theory (Spring 2025)

Logistics: TTh 2-3:30
GDC 4.516 (ignore the CMA room listed by the registrar)
Unique Number: 51395
Course web page: http://www.cs.utexas.edu/~diz/395T/25/
Professor: David Zuckerman
Email: diz@cs.utexas.edu
Office: GDC 4.508
Phone: 471-9729
Office Hours: W 3-4, Th 3:30-4:30
TA: Vinayak Kumar
Email: vmkumar@cs.utexas.edu
Office Hours: M 4-5, GDC 4.416
Course Overview: Spectral graph theory studies how algebraic properties of matrices, most notably eigenvalues and eigenvectors, give information about the combinatorial structure of graphs, such as connectivity. This course will focus on uses of spectral graph theory in theoretical computer science. One example is constructing highly-connected graphs called expanders. Another example is a way to sparsify a graph so that it maintains key properties. This enables speed-ups of certain algorithms, since the algorithm output for the sparse graph gives information about the original graph.

I plan to cover the following topics:

  • Basics (2 weeks): Eigenvalues and graph properties, examples, Laplacian, eigenvalue interlacing.
  • Expanders and Extremal Graphs (3 weeks): Cayley graphs, strongly regular graphs, measures of expansion, expander mixing lemma, Cheeger's inequality, expander constructions.
  • Random Walks (2 weeks): Random walks on undirected graphs, application to randomness-efficient error reduction, random walks on directed graphs and PageRank.
  • Partitioning and Clustering (1-2 weeks): Graph coloring, higher order Cheeger inequalities, algorithms.
  • Graph Sparsification (2 weeks): Effective resistance, cut sparsifiers, spectral sparsifiers, matrix Chernoff bound, algorithms.
  • Solving Laplacian Systems (1 week): Iterative methods for linear systems, preconditioning.
  • High Dimensional Expanders (2-3 weeks): Definitions, local to global, random walks, connection to sampling, constructions.

I will draw on a variety of material, including the following books and lecture notes:
Dan Spielman
Luca Trevisan
Salil Vadhan
David Williamson
van Lint and Wilson
Norman Biggs

Prerequisites: Mathematical and computational maturity, plus familiarity with the following topics:
  • Algebra, including vector spaces, eigenvectors/eigenvalues, and finite fields;
  • Discrete Probability, including moments and deviation bounds of Markov, Chebyshev, and Chernoff;
  • Combinatorics, at least basic combinatorics and graph theory;
  • Algorithms, including their analysis and the notion of polynomial time.
Grading: 90%: Homework (3-5 problem sets)
10%: Participation
Homework policy: I anticipate assigning three to five problem sets.

Collaboration policy: While you should first think about the problems on your own, you are encouraged to discuss the problems with your classmates. Please limit your collaborations on any particular homework to at most three other students. Discussion of homework problems may include brainstorming and verbally walking through possible solutions, but should not include one person telling the others how to solve the problem. In addition, each person must write up their solutions independently, and these write-ups should not be checked against each other or passed around or emailed. You must acknowledge any collaboration by writing your collaborators' names on the front page of the assignment. You don't lose points by having collaborators.

Citation policy: Try to solve the problems without reading any published literature or websites, besides the class text and links off of the class web page. If, however, you do use a solution or part of a solution that you found in the literature or on the web, you must cite it. Furthermore, you must write up the solution in your own words. You will get at most half credit for solutions found in the literature or on the web.

Use of AI: You may not use ChatGPT or any AI to help you solve homework problems.

Submission policy: Each student has two late days that they can use during the semester with no penalty (one assignment two days late, or two assignments one day late). Once late days are used up, half credit will be given for assignments that are at most a day late. A day here means 24 hours. The weekend doesn't count, so Friday to Monday counts as one day. I may not allow late days for certain assignments.
Canvas: We will use Canvas, which contains Ed Discussion. Homeworks and grades will be posted on Canvas. We will use Ed Discussion for class discussion. Instead of emailing questions to the teaching staff, please post your question to Ed Discussion.
Laptops/Phones: The use of laptops and mobile devices is generally prohibited, but I will allow the use of tablets if you use them only for class-related purposes. All phones must be silenced.
Students with
Disabilites:
Any student with a documented disability (physical or cognitive) who requires academic accommodations should contact the Services for Students with Disabilities area of the Office of the Dean of Students at 471-6259 (voice) or 471-4641 (TTY for users who are deaf or hard of hearing) as soon as possible to request an official letter outlining authorized accommodations.

Last modified: January 15, 2025.