Summary

In this chapter, we explored integrating LLMs with graph learning, highlighting how LLMs can enhance traditional GNNs. We discussed the evolution of LLMs, their capabilities in processing textual data within graphs, and their potential to improve node representations and graph-related tasks. You learned about various approaches for utilizing LLMs in graph learning, including feature-level and text-level enhancements, as well as using LLMs as predictors through techniques such as InstructGLM.

We also presented real-world applications in drug discovery, social network analysis, and financial fraud detection to illustrate the practical benefits of this integration. Furthermore, you became familiar with the challenges in combining LLMs with graph learning, such as computational costs, data bias, and explainability issues, while learning about the potential for future advancements in this field.

In the next chapter, we’ll explore applying deep learning to graphs in...