66123 Saarbrücken (Germany)
Trustworthy Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a fundamental building block in many artificial intelligence systems. Even beyond uses where the graph structure is explicit (e.g. social networks), they show impressive performance for general object-oriented perception, representation, and reasoning. In this seminar we will cover GNNs that are not only accurate or efficient, but also robust, privacy-preserving, fair, uncertainty-aware, and explainable. We will explore how GNNs fail w.r.t. these trustworthiness aspects and how to improve them.
Each student will receive a few research papers on a single topic which they should carefully read and analyze. Starting from these initial papers they should explore the surrounding literature and summarize their main ideas and findings in a 4-page seminar paper. Students will also participate in a peer-review process where they have to provide constructive feedback on each other's work (1 page review for 3 other papers). Finally, each student will prepare and deliver a presentation about their topic during a block seminar at the end of the semester.
Exact dates and times will be determined soon.
More details and the final list of topics will be provided in the kick-off meeting.