Organisation:
Prof. Dr. Verena Wolf
Gerrit Großmann, MSc
Please contact Gerrit Großmann for questions about the seminar and have [GNNSeminar] in the subject line.
Please use the seminar assignment system to register.
Graphs and Networks are ubiquitous in nature, and graph-structured data naturally occurs in a variety of disciplines including physics, chemistry, neuroscience, biology, social science, and epidemiology.
This seminar addresses the problem of learning from graph-structured data. Traditional machine learning and deep learning techniques are not well suited to process data containing rich relational information. Graph Neural Networks (GNNs), Graph Machine Learning, and Geometric Deep Learning offer an interesting perspective on this challenge and are currently one of the fastest-growing topics in ML research. GNNs can assist in discovering drugs, fighting cancer, detecting neurological disorders, analyzing complex systems, recommending friendships in online social networks, controlling traffic, as well as studying epidemiological processes.
We examine recent developments in graph ML research, study state-of-the-art techniques, discuss their limitations, and explore various applications.
A neat overview of GNNs is given in the Graph Neural Networks with Petar Velickovic talk.
A basic understanding of machine learning and deep learning will be helpful but is not mandatory. If you are new to the field, the 3blue1brown Neural Network Playlist might be a good start. For further consultation of general concepts, we refer the reader to the great Deep Learning Book by Goodfellow et al. (available online). Additional material on GNNs can be found in the Graph Representation Learning Book by Hamilton (available online). There are many more good introductory GNN and ML talks on youtube.
To pass, you have to attend all sessions, give a presentation, participate in discussions, and submit reports. Furthermore, we expect all participants to read all seminar papers in the course of the semester and write down questions/thoughts/ideas/etc (of course, we do not expect you to understand them fully) . Each participant must also give an ungraded mock presentation to their group (i.e., A, B, or C) (at least 2 weeks prior to the actual presentation).
The final grade consists of: your presentation (40%), reports (40%), and participation during the seminar discussions (20%).
Identify the key ideas and concepts and give a self-consistent presentation explaining these concepts to your fellow students. The presentation should focus on intutivion and high-level understanding and contextualization, not on technical details. We encourage the students to read related work and explore supplementary material and include it to the presentation where it seems useful (e.g., from related literature, youtube, github, medium articles, OpenReview, etc.). The presentation should be 20 to 30 minutes long.
Each student has to submit four short reports (one for each paper of his/her group, 1000-2000 words). The report should contain a short summary of (what you consider to be) the main contribution or most intriguing idea of the paper. Otherwise, you can freely express your own thoughts on the topic. For instance: What did you like/dislike about the paper (both methodically and didactically)? What are connections to other seminar papers? Can you suggest improvements? What do you think is missing? Can you think of other useful applications?
You can use the Springer LNCS Latex template (every other reasonable formatting is fine, too).
Read each presented paper in advance (of course, we do not expect you to understand them fully) and actively participate in the discussion. We expect each participant to attend each session.
Foundations
| Date | Timeslot | Student | Topic | Group |
|---|---|---|---|---|
| 23.09.2021 | 10:00 | Safya Alzayat | Semi-Supervised Classification with Graph Convolutional Networks | A |
| 23.09.2021 | 10:45 | Suruthai Noon Goldstein | Inductive Representation Learning on Large Graphs | B |
| 23.09.2021 | - | Siwen Chen | Neural Message Passing for Quantum Chemistry | C |
Applications
| Date | Timeslot | Student | Topic | Group |
|---|---|---|---|---|
| 23.09.2021 | 11:30 | Gilbert El Khoury | BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis | A |
| 23.09.2021 | 13:00 | Divya Nidadavolu | Fake News Detection on Social Media using Geometric Deep Learning | B |
| 23.09.2021 | 13:45 | Roman Joeres | Modeling polypharmacy side effects with graph convolutional networks | C |
| 24.09.2021 | 10:00 | Maryam Meghdadi Esfahani | Neural Relational Inference for Interacting Systems | A |
| 24.09.2021 | 10:45 | Yan Yan Lau | Finding Patient Zero: Learning Contagion Source with Graph Neural Networks | B |
| 24.09.2021 | 11:30 | Aakash Rajpal | MolGAN: An implicit generative model for small molecular graphs | C |
Properties, limitations, and recent advances
| Date | Timeslot | Student | Topic | Group |
|---|---|---|---|---|
| 24.09.2021 | 12:45 | Adarsh Jamadandi | How Powerful are Graph Neural Networks? | A |
| 24.09.2021 | 13:30 | Vabuk Pahari | Strategies for Pre-training Graph Neural Networks | B |
| 24.09.2021 | 14:15 | Zhifei Li | Adversarial Attacks on Neural Networks for Graph Data | C |
The participants are split into three groups (A,B,C - with four participants each). Each student writes four reports, one for each paper of his/her group. The mock presentations are also given within these groups.