Speaker
Mathieu Guillame-Bert
(Google Zurich)
Description
Graph Neural Networks (GNNs) are a powerful paradigm for Neural Net ML models to operate on relational data or data with structural information. This talk explores the practical use and ongoing research on GNN done at Google for industrial applications. We provide a brief overview of GNNs modeling, including GCNs, Graph Transformers, and geometric-aware models. Then we discuss a variety of real-world applications. Finally, we talk about scaling challenges on very large graphs, dynamic graphs, and fast inference on specialized hardware acceleration.
