Summary
This chapter has provided an introduction to combining GraphML and LLMs with practical examples. By leveraging the strengths of both technologies, researchers and practitioners can push the boundaries of what’s possible in AI-driven applications.
We have learned what LLMs are and how they can work with graphs using state-of-the-art techniques. We also explored the current trends and challenges in the landscape of GraphML and LLM integration. Finally, we saw how to start developing useful tools such as knowledge graph builders and GraphRAG systems.
In the next chapter, we will turn to some recent developments and the latest research and trends in machine learning that have been applied to graphs. In particular, we will describe some of the latest techniques (such as generative neural networks) and applications (such as graph theory applied in neuroscience) available in the scientific literature, providing some practical examples and possible applications.
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