Graph Neural Networks (GNNs) have emerged as one of the most popular areas of research in the field of machine learning and artificial intelligence. The core idea is to explore the relationships among data samples to learn high-quality node, edge, and graph representations. In just the span of a few years, GNNs have expanded from mostly theoretical and small-scale studies to providing state-of-the-art solutions to many problems arising in diverse application domains. This includes domains that traditionally relied on graph learning (e.g., information retrieval, recommendations, fraud detection, knowledge representation), to science and engineering domains whose underlying data can be naturally represented via graphs (e.g., chemistry, bioinformatics, drug discoveries, material science, physics, circuit design), and to areas of science and engineering that have not traditionally been the domain of graph methods (e.g., computer vision, natural language processing, computer graphics, reinforcement learning).
31:47 Graph Neural Networks For Learning About Never Before Seen Phenomena by Marinka Zitnik (Harvard)
1:24:17 Graphcore’s IPU and GNNs by Gianandrea Minneci (Graphcore)
2:19:31 Graph Representation Learning for Chip Design by Azalia Mirhoseini (Google)
2:58:05 High Performance GNNs in JAX by Jonathan Godwin (DeepMind)
5:59:59 GNNs for Charged Particle Reconstruction at the Large Hadron Collider by Savannah Thais (Princeton)
6:40:19 Machine Learning on Dynamic Graphs: Temporal Graph Networks by Emanuele Rossi (Imperial/Twitter)
7:39:26 Efficient GNNs: How Can Graphs Go From Last To Fast? by Nicholas Lane (Cambridge)
8:20:25 Graph Neural Networks: Moving from Research to Commercial Applications by George Karypis (University of Minnesota/AWS)
