Explore topological data analysis in graph representation learning through this 57-minute lecture by Bastian Rieck. Delve into graph classification tasks using machine learning techniques, with a focus on incorporating topological features. Discover a novel 'topology-aware' layer for graph neural networks and its impact on theoretical expressivity. Gain insights into persistent homology, multifiltration learning, and experimental results on synthetic datasets. Suitable for TDA enthusiasts, with helpful but not required prior knowledge of machine learning techniques.