Graph Data Science

Explore new ways of working with your data

The Neo4j Graph Data Science (GDS) library provides efficiently implemented, parallel versions of common graph algorithms, exposed as Cypher procedures. Additionally, GDS includes machine learning pipelines to train predictive supervised models to solve graph problems, such as predicting missing relationships.

Graph Data Science Library

GDS

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Learn how to use machine learning pipelines to train supervised models using Neo4j’s data science library.

/docs/graph-data-science/

Graph Data Science Python Client

GDS

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Keep using Python as your primary language and environment to project graphs.

/docs/graph-data-science-client/

Keep exploring

Here are some recommended resources to get started with Graph Data Science:

  1. Install the GDS library to your Neo4j Graph Database

    Learn how to install the GDS plugin according to your deployment option.

  2. GraphAcademy: Introduction to Neo4j Graph Data Science

    Gain a high-level technical understanding of the Neo4j Graph Data Science (GDS) library.

  3. Tutorial: Import from Pandas

    Learn how to create a graph construct by importing your dataset from Pandas.