Graph Neural Networks (GNNs) are a tantalizing way of modeling data which doesn't have a fixed structure. However, getting them to work as expected has had some twists and turns over the years.

This talk will have four components.

1. First, we'll briefly describe the Graph Mining team at Google.
2. Next, we'll focus on the Graph Mining team's work to make GNNs useful. We'll focus on challenges that we've identified and the solutions we've developed for them. Specifically, we'll highlight work that's led to more expressive graph convolutions, more robust models, and better graph structure. In addition, we'll highlight some new features available in our open source library for GNNs in TensorFLow, TF-GNN.
3. Second, we'll talk about other advances in Graph Mining from the group (including clustering, graph building, and privacy).
4. Finally, we'll have a tutorial section with quick demos covering TF-GNN, synthetic evaluation of GNNs with GraphWorld, and benchmarking for clustering tasks.