| Instructor: | A. Erdem Sariyuce (erdem AT buffalo.edu) |
| Class hours: | Wed 5:00-7:00, Norton 213 |
| Office hours: | Wed 3:30-4:30, Davis 323 |
Graphs are everywhere. Their scale, rate of change, and the irregular nature pose many new challenges. Deep learning has been shown to be successful in a number of domains, ranging from images to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics. This seminar course covers recent papers in the last few years about deep learning on graphs. We will consider graph embeddings, knowledge graphs, graph kernels, graph neural networks, graph convolutional networks, graph adversarial methods. Students will learn the literature on deep learning on graphs, understand the state-of-the-art algorithms on various problems, and be familiar with the recent trends.
It is assumed that students have a solid background on discrete mathematics and algorithms. Basic research skills like paper reading, critical thinking, problem solving, report writing, communication, and presentation are important as well.
The final grade is S/U and 80 pts score is needed for an S.
Each week there will be 3 or 4 "explainers". Those students are responsible for presenting the paper and answering the student questions on Piazza. Instructor will pick the presenter among those explainers at the beginning of the class. All explainers must be ready to present. Assignment of the explainers for each week will be done by instructor.
Each week all the students are supposed to read the paper of the week. Each student (except explainers) will ask a unique question (one question, not less, not more) on Piazza. Those questions will be answered by the explainers on Piazza and some questions will be selected by them for discussion in class. Questions should be open-ended and provide ground for class discussions, i.e., 'can you explain alg 1?' is not that kind of question. Questions should be posted to Piazza by Monday night, 11.59 pm EST.
The schedule for the first two weeks is below. Rest will be posted soon.