8 Considerations for GNN projects
In this chapter, we describe the practical aspects of working with graph data, as well as how to convert nongraph data into a graph format. We’ll explain some of the considerations involved in taking data from a raw state to a preprocessed format. This includes turning tabular or other nongraph data into graphs and preprocessing them for a graph-based machine learning package. In our mental model, shown in figure 8.1, we are in the left half of the figure.
Figure 8.1 Mental model for graph training process. We’re at the start of the process, where we prepare our data for training.
We’ll proceed as follows. In section 8.1, we introduce an example problem that might require a graph neural network (GNN) and how to proceed with tackling this project. Section 8.2 goes into more detail on how to use nongraph data in graph models. We then put these ideas into action in section 8.3 by taking a dataset from a raw file to preprocessed data, ready for training. Finally, ideas for finding more graph datasets are given in section 8.4.