Here is a book, than in my opinion has aged well and is still very relevant and worth reading. Graph Representation Learning (Springer, 2020), by William L. Hamilton, is an introduction to graph neural networks.
Graph representations and the more general geometric interpretation of deep learning provide the basic principles necessary to understand and generalize to the most relevant architectures, including CNNs, transformers, and, of course, GNNs.
