Common graph applications
We will now look at some common applications of GNNs. Typically, applications fall into one of the three major classes listed below. In this section, we will see code examples on how to build and train GNNs for each of these tasks, using TensorFlow and DGL:
- Node classification
- Graph classification
- Edge classification (or link prediction)
There are other applications of GNNs as well, such as graph clustering or generative graph models, but they are less common and we will not consider them here.
Node classification
Node classification is a popular task on graph data. Here, a model is trained to predict the node category. Non-graph classification methods can use the node feature vectors alone to do so, and some pre-GNN methods such as DeepWalk and node2vec can use the adjacency matrix alone, but GNNs are the first class of techniques that can use both the node feature vectors and the connectivity information together...
