Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-de_ned heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. In this course, we provide a technical introduction to key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, graph neural networks, and graph generation. In doing so, we develop a unied framework to describe these recent approaches, and we emphasize real-world applications involving large-scale social and biological networks.
Organizer: Professor Bin Yang, e-mail: byang@cs.aau.dk
Lecturers: Professor Jian Tang, Montreal Institute for Learning Algorithms
ECTS: 2,0
Time: 7, 8, 9 and 10 December 2020 from 14:00-16:00 (ONLINE)
Place: ONLINE
Zip code: 9220
City: Aalborg
Number of seats: 30
Deadline: 16 November 2020
Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
- Teacher: Bin Yang