Summary

In this chapter, you learned about how to use GDS pipelines to simplify the processes of training an ML model involving graph-based features. GDS pipelines can be configured to run graph algorithms such as the Louvain algorithm and use the result as a feature in a classification or regression model. These models are part of the GDS, so we do not have to explicitly extract data from Neo4j and use another ML library. Everything can be run using the projected graph, which is stored in the model and pipeline catalogs, and used to make predictions on unseen nodes. This lets us use a single tool to compute graph features and perform ML tasks, including the training and prediction of different models, without explicit data exchange from and to the database.

Additionally, we played with the embedding algorithms included in the GDS, starting to surface their advantages and disadvantages.

In the next chapter, we will use another type of pipeline from the GDS to solve another kind...