Summary

In this chapter, we have used both Python and Neo4j to create, store, and analyze graph projections, demonstrating their power in both the efficiency and interpretability of results. Each technology has its own separate strengths. In Neo4j, we have less readily available access to complex graph data science algorithms to analyze our projection, compared to what we can easily carry out in Python with igraph.

However, using Neo4j is a more permanent storage option and suitable for a projection we might want to repeatedly read and write to. For any given use case, it is important to consider what the most appropriate projection creation and storage tool is for the task at hand.

These skills you have acquired will allow you to navigate between Neo4j, Python, and igraph with ease and will have set a strong foundation to build pipelines between the two technologies – a happy marriage indeed.

In the next, and final, chapter, you will learn about some of the common...