Summary

This is the end of this chapter, where you were introduced to the method you can use to extend GDS and take advantage of all the common features we are looking for when dealing with graph analytics: memory and CPU performance. The projected graph and GDS internal management of job batches are easily accessible to us if we write a couple of Java classes.

We also studied the PageRank algorithm and implemented two versions of it: one relying only on the maximum number of iterations as stopping criteria, and another version that considers the stability of computed scores compared to the previous iteration, within a certain tolerance. We also learned how to unit test our algorithm by writing a simple test that runs our algorithm on a sample graph, which we were able to define by writing a Cypher CREATE statement.

This chapter is also the end of this book! We have come a long way since Chapter 1, Introducing and Installing Neo4j, where we introduced the concept of graphs, and...