Graduate Seminar on Machine Learning: Machine Learning with Graphs

Logistics

Instructor: Arlei Silva

Lectures: Monday 2:00-2:50, room: DCC 113, building: Duncan Hall

Summary

Machine learning on graphs (MLG) is an exciting and growing research topic mainly for two reasons: (1) Many relevant real-world problems (in recommendation, infrastructure, healthcare, etc.) have some structure that can be captured as nodes, edges, and their attributes; and (2) machine learning has been the hottest topic in computer science in the past decade, so it has impacted the entire field. In this seminar, we will discuss some of the most recent papers on deep learning on graphs (Graph Neural Networks), which arguably has become the go-to approach for MLG. Many of these developments can be posed as 'How can I do X on a graph?', where 'X' might be convolution, pooling, reinforcement learning, etc. However, deep learning also provides a common framework with the potential to solve most (if not all) problems in network science, graph mining, and even graph algorithms, by fitting well-designed functions using data (sometimes lots of it). In the last five years, three books, about 20 surveys, and hundreds of journal and conference papers supporting this claim have been published. This seminar is an opportunity to read and discuss the state-of-the-art in deep learning on graphs.

Format

The seminar will be composed of an introductory lecture and paper presentations with discussions. For the presentations, we will adopt a role-based format instead of the most traditional one. The discussion of each paper will be lead by a group of 4 students, each of them will select one of the following roles:
  1. Presenter: Provides an intuitive and concise description of the problem and key ideas of the paper (25 minutes)
  2. Reviewer: Writes a conference-style review and share it with the instructor before class
  3. Researcher: Tries to propose a novel research problem based on the paper
  4. Archeologist: Briefly discusses important previous and future work (if any) related to the paper (mini-survey)
  5. Hacker: Tries to find an implementation of the proposed approach and run (very) simple experiments
  6. Biographer: Tells a bit about the authors of the paper (names, backgrounds, research interests, affiliations, other publications, etc.)
  7. Ethicist: Discusses possible ethical issues (privacy, fairness, manipulation, etc.) related to the paper
  8. Practitioner: Describes how far is the proposed approach to be deployed as a technology
Roles 1-2 should be selected for each paper and 3-8 are optional. Role 2 will share the review with the instructor before class and the review will be shared with everyone after the class. Roles 3-8 have 5 minutes to summarize their work during the class (slides are optional). Students should not repeat roles during the semester but can perform multiple roles for the same paper if desired (other students will have priority for the second, third, and fourth roles). The remainder of the class participates in the discussion and submits a very short (like a paragraph/abstract) summary of the paper (problem, the idea of the approach, relevance, results, etc.) before the class. One paper will be presented per lecture (25-minute presentation, 5 minutes for roles 3-8 roles, 15-minute discussion). All students are expected to contribute to the discussion (with questions, comments, etc.). The expectation is that each student will perform 4 different roles throughout the semester.

Syllabus

Candidate Reading list

References

Other resources