Summary
In this chapter, we introduced the concept of temporal graph machine learning. We discovered why it is needed and what the main problems that can be addressed using this paradigm are. We also learned a taxonomy for classifying temporal graph machine learning algorithms. Finally, we explored practical examples to understand how the theory can be applied to practical problems.
In the next chapter, we will explore the integration of language models with graphs, a rapidly evolving area at the intersection of natural language processing and graph-based learning. We will discuss recent advancements in leveraging graph structures to enhance language models, as well as techniques that incorporate textual data into graph-based representations.
