Organizers: Christian Koke, Abhishek Saroha

Please forward any queries related to the seminar to: Abhishek.Saroha@in.tum.de.

The field of graph learning has recently witnessed a significant surge in interest with with emerging applications of networks on graph structured data ranging from topics in drug discovery over traffic control to guiding mathematicians intuition in the process of postulating new conjectures and and proving new theorems. This seminar focuses on recent research and emergent paradigms in graph learning. Possible presentation topics range from mathematical foundations to architectures tailored to specific applications such as e.g. molecular property predictions.

UPDATE: The preliminary meeting slides (WS24) have been uploaded.

• Good knowledge of basic mathematics such as linear algebra, analysis, probability and numerics, graph theory etc. is required.

• Participation in at least one of the offered deep learning lectures at TUM is highly recommended. For e.g. 1, 2, 3 etc.

The seminar will take place in a block format on 27th and 28th May completely online via Zoom. After the kick-off meeting in early April, the students will be given to choose from a list of recent works in graph learning and present their findings as a short presentation during the seminar. Afterwards, they will be required to compile their results as a short report to be submitted towards the end of the semester.

The list of available papers can be found here (password protected).