Thanks for purchasing Graph Neural Networks in Action. If you want a resource to understand and implement GNNs, this book is for you! When I was dipping my toes into GNNs, most resources were tutorials on Medium.com, GNN library documentation, some papers with code, and a few scattered videos on youtube. These were useful sources of knowledge, but learning from them was inefficient.
These resources were scattered about the internet. And, in many of them, there were unspoken assumptions and prerequisites that presented roadblocks to comprehensive understanding. Finally, many tutorials and explanations lacked the nuance to allow me to answer the questions of discernment like, ‘why use a GCN over GraphSage ?’.
I wrote GNNs in Action to remedy many of these pain points. I structured it to be both a book where you can jump into a topic of interest and get something down in code, and as a reference that comprehensively covers the relevant knowledge that would allow one to employ and discuss GNNs with confidence. This is a hard balance to strike, and I hope to have done it well.
Speaking of implementation, one other thing that bothers me is when a book uses frameworks and languages that I don’t use. Though I use python exclusively as the programming language, examples can be found that are grounded in the two major frameworks, pytorch and tensorflow. For smaller datasets, I use Pytorch Geometric and DGL; for scaled problems, I use Alibaba’s Graphscope, as well.