Summary
In this chapter, we introduced the MPNN framework to generalize GNN layers using three steps – message, aggregate, and update. In the rest of the chapter, we expanded this framework to consider heterogeneous networks, composed of different types of nodes and edges. This particular kind of graph allows us to represent various relations between entities, which are more insightful than a single type of connection.
Moreover, we saw how to transform homogeneous GNNs into heterogeneous ones thanks to PyTorch Geometric. We described the different layers in our heterogeneous GAT, which take node pairs as inputs to model their relations. Finally, we implemented a heterogeneous-specific architecture with HAN and compared the results of three techniques on the DBLP dataset. It proved the importance of exploiting the heterogeneous information that is represented in this kind of network.
In Chapter 13, Temporal Graph Neural Networks, we will see how to consider time in GNNs...
