Digging into the Neo4j GDS library
The GDS library was first released in 2020. It was the successor of the Graph Algorithm plugin, which first appeared in 2019. Since then, a lot of improvements have been performed in terms of performance and standardization, and a lot of new features have been added, both in terms of algorithm parametrization and new kinds of algorithms. In the following subsections, we give an overview of its content and working principles.
GDS content
As the name suggests, the GDS library contains tools to be used in a data science project using data stored in Neo4j. This includes the following:
- Path-related algorithms
- Graph algorithms
- Machine learning (ML) models and pipelines
- Python client
Let’s talk in a bit more detail about each of these aspects, to understand when and where they are useful.
Path-related algorithms
In graph theory, traversing a graph to find specific paths from one node to another (typically the...
