Putting the projection to work

We have two projections to put to use now, one read from Neo4j and converted to a Python igraph and one stored more permanently in Neo4j, alongside the original graph data. It’s time to generate some insights about the films and actors in our knowledge graph, drawing on our projections. Now that our projections have a nice, simple, and clean schema to work with, our analysis can be more powerful than if we approached the original knowledge graph data directly. Let’s begin by returning to Python and our co-star graph.

Analyzing the igraph actor projection

As a reminder, we used Python and the Neo4j API to query our knowledge graph using Cypher and return actors who starred alongside each other in the same film. We then converted our results to an edgelist and imported this into igraph, ready for graph analytics. The analysis steps are as follows:

1. Let’s start with the basics and learn about some of the properties of our...