Summary

In this chapter, you have learned about the LP problem, an ML technique that’s only possible with graph data. It can be used in many contexts to predict future or unknown links between any type of nodes, as long as we have some example or context data. You have learned how to build an LP pipeline with Neo4j’s GDS, which takes care of negative observation sampling, model training, and storage for us.

This chapter is the last one where we will talk about predictions and ML. Overall, we have studied several use cases for ML on graphs, including node classification and future/unknown LP. You have learned how to extract graph-based features or embeddings to feed an ML model in your preferred library (we’ve used scikit-learn). You have also learned that the whole ML pipeline can be managed within Neo4j and its GDS library thanks to built-in pipelines and models.

GDS contains many interesting tools, but it is generally still young compared to other ML tools...