GNNs and the need for a separate vertical

We won’t dive into the details of what GNNs do or how they differ from other popular neural network architectures in this chapter. Here, we’ll merely attempt to explain why there’s a need to study GNNs separately from other deep learning architectures.

Before talking about the differences, we must discuss the similarities. GNNs are an architecture choice that’s specialized for processing graph data and outputting representations or node embeddings. Similar to how convolutional networks are fundamental for reading pixel data, the set of architectures under GNNs are optimized for reading graph data. GNN-based learning tasks follow the same trajectory as other deep learning solutions: to iteratively optimize the parameters of the model so that a loss function can be minimized. In the case of GNNs, the loss function often tries to capture and preserve meaningful information about the graph structure.

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