Graph Neural Networks for Complex Graphs

In the earlier chapters, we have discussed graph neural network models focusing on simple graphs where the graphs are static and have only one type of nodes and one type of edges. However, graphs in many real-world applications are much more complicated. They typically have multiple types of nodes, edges, unique structures, and often are dynamic. As a consequence, these complex graphs present more intricate patterns that are beyond the capacity of the aforementioned graph neural network models on simple graphs. Thus, dedicated efforts are desired to design graph neural network models for complex graphs. These efforts can significantly impact the successful adoption and use of GNNs in a broader range of applications. In this chapter, using complex graphs introduced in Section 2.6 as examples, we discuss the methods to extend the graph neural network models to capture more sophisticated patterns. More specifically, we describe more advanced graph filters designed for complex graphs to capture their specific properties.