We recently released a graph algorithms library for Neo4j.

You can use these graph algorithms on your connected data to gain new insights more easily within the transactional database and to improve query results from your graph data, for example by focusing on particular communities or favoring popular entities.

We developed this library as part of our effort to make it easier to use Neo4j for a wider variety of applications. Many users expressed interest in running graph algorithms directly on Neo4j without having to employ a secondary system.

We also tuned these algorithms to be as efficient as possible in regards to resource utilization as well as streamlined for later management and debugging.

In this session, we'll look at some of these graph algorithms and the types of problems that you can use them for in your applications and the implementation choices we made.