When I first started learning Python I came across the NetworkX library and always enjoyed using it to run graph algorithms against my toy datasets.
Nowadays Neo4j has its own Graph Algorithms library but we have to call that via Cypher procedures which isn’t quite as nice. I wanted to fix that.
As a result, a few months ago I started writing a NetworkX-esque API that would provide a nice wrapper around Neo4j’s algorithms. In this talk I’d like to show off the library and how easy it is to use the networkx function calls that you’re used to without having to worry whether your graph will fit in memory in your Python program.
Speaker: Michael Hunger @ GraphConnect 2018