Graph Data Science

Explore new ways of working with your data

The Neo4j Graph Data Science (GDS) library provides implementations 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 run graph algorithms and use machine learning pipelines to train supervised models using Neo4j’s data science library.

https://neo4j.com/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.

https://neo4j.com/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.