Graph neural networks are the quintessential neural network for geometric deep learning, and, as the name suggests, they work particularly well on graph-based data such as meshes.
Now, let's assume we have a graph, G, that has a binary adjacency matrix, A. Then, we have another matrix, X, that contains all the node features. These features could be text, images, or categorical, node degrees, clustering coefficients, indicator vectors, and so on. The goal here is to generate node embeddings using local neighborhoods.
As we know, nodes on graphs have neighboring nodes, and, in this case, each node tries to aggregate the information from its neighbors using a neural network. We can think of the network neighborhood as a computation graph. Since each node has edges with different nodes, each node has a unique computation graph.
If we think back to convolutional...