Learning from Heterogeneous Graphs

In the previous chapter, we tried to generate realistic molecules that contain different types of nodes (atoms) and edges (bonds). We also observe this kind of behavior in other applications, such as recommender systems (users and items), social networks (followers and followees), or cybersecurity (routers and servers). We call these kinds of graphs heterogeneous, as opposed to homogeneous graphs, which only involve one type of node and one type of edge.

In this chapter, we will recap everything we know about homogeneous GNNs. We will introduce the message passing neural network framework to generalize the architectures we have seen so far. This summary will allow us to understand how to expand our framework to heterogeneous networks. We will start by creating our own heterogeneous dataset. Then, we will transform homogeneous architectures into heterogeneous ones.

In the last section, we will take a different approach and discuss an architecture...