Machine Learning with Graphs


Instructor: Arlei Silva
Lectures: Tuesday, Thursday 2:30-3:45 DCH 1070 (Duncan Hall)
Office hours: Monday 2:30-3:30, Wednesday 10:00-11:00 DCH 3001 (Duncan Hall)
Teaching assistants: Guanchu Wang (, Ruixiang Tang (

Course description

Graphs show up in machine learning in many forms. Oftentimes, the input data can be naturally represented as a graph, such as for relational learning tasks applied to social networks and graph kernels applied to chemical data. Other times, graphs are just a framework to express some intrinsic structure in the data, such as for graphical models and non-linear embedding. In both cases, recent advances in representation learning (or graph embedding) and deep learning have generated a renewed interest in machine learning on graphs. This course will overview both traditional and more recent graph-based machine learning algorithms.

At the end of the course, students are expected to be able to: (1) identify the appropriate graph-based machine learning algorithm for a given problem; (2) extend existing algorithms to solve new related problems; and (3) recognize some of the key research challenges in the field.

The course will be a mixture of lectures, student presentations, homework assignments (including programming), and a hands-on semester-long project.

Credit hours: 3

Recommended prerequisites

Undergraduate-level linear algebra, undegraduate-level probability and statistics, basic Python programming.

Course materials

No textbook is required. Lecture notes will be shared for reference after the class. The notes will be based on a recommended list of references.



Homeworks and reports should be submitted on Canvas. Questions should be submitted on Piazza. For personal matters, please email the instructor or the TAs.

Rice honor code

Students are expected to adhere to the Rice Honor Code. You are encouraged to collaborate and to find resources online. However, all the material to be graded is expected to be original. The exceptional use of someone else's work, such as a figure, should be properly recognized.

Students with disabilities

If you have a documented disability that may affect academic performance, you should: 1) make sure this documentation is on file with Disability Support Services (Allen Center, Room 111 / / x5841) to determine the accommodations you need; and 2) meet with me to discuss your accommodation needs.

Related courses at Rice



  1. Christopher M Bishop. Pattern Recognition and Machine Learning. Springer, 2006

  2. Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The elements of statistical learning. Springer, 2009

  3. Lise Getoor and Ben Taskar. Introduction to statistical relational learning. MIT press, 2007

  4. John A Lee and Michel Verleysen. Nonlinear dimensionality reduction. Springer, 2007

  5. Fan RK Chung Spectral Graph Theory. AMS, 1997

  6. Swarnendu Ghosh, Nibaran Das, Teresa Goncalves, Paulo Quaresma, and Mahantapas Kundu. The journey of graph kernels through two decades. Computer Science Review, 27:88-111, 2018

  7. 111, 2018.
  8. Daniel Spielman. Spectral and Algebraic Graph Theory. Incomplete Draft, 2019

  9. Mark Newman. Networks. Oxford university press, 2018

  10. William L. Hamilton. Graph representation learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2020