GraphCast
Graphcast is a graph neural network architecture with an encoder-decoder configuration. The graph neural network is used to encode unstructured input data into a graph representation. As opposed to, for instance, convolutional layers where neighbouring information is encoded in a structured grid, graph layers use message passing between nodes to capture the relationships between different parts of the input data. This allows for the encoding of different kind of information, not necessarily restricted to a grid configuration.
One important hyperparamter to be set in this kind of architectures is the number of hops the messages containing neighbouring information are allowed to travel. This is crucial for the model to learn from the correct amount of knowledge, and allows for reducing the computational complexity of the model, as the number of hops is directly related to the time required for the model to train.
