  Many physical systems can be best understood as sets of discrete data with
associated relationships. Where previously these sets of data have been
formulated as series or image data to match the available machine learning
architectures, with the advent of graph neural networks (GNNs), these systems
can be learned natively as graphs. This allows a wide variety of high- and
low-level physical features to be attached to measurements and, by the same
token, a wide variety of HEP tasks to be accomplished by the same GNN
architectures. GNNs have found powerful use-cases in reconstruction, tagging,
generation and end-to-end analysis. With the wide-spread adoption of GNNs in
industry, the HEP community is well-placed to benefit from rapid improvements
in GNN latency and memory usage. However, industry use-cases are not perfectly
aligned with HEP and much work needs to be done to best match unique GNN
capabilities to unique HEP obstacles. We present here a range of these
capabilities, predictions of which are currently being well-adopted in HEP
communities, and which are still immature. We hope to capture the landscape of
graph techniques in machine learning as well as point out the most significant
gaps that are inhibiting potentially large leaps in research.
