neo4j/graph-data-science

Source code for the Neo4j Graph Data Science library of graph algorithms.

What it solves

It provides a specialized library for performing advanced analytics on graph-structured data. It allows users to move beyond simple database queries to execute complex graph algorithms and machine learning pipelines directly within a Neo4j database environment.

How it works

The library acts as a plugin for the Neo4j DBMS. It integrates graph algorithms, transformations, and machine learning pipelines that can be operated using Cypher procedures. It also provides a Pregel API for developers to build custom algorithms and a Python client (graphdatascience) that allows users to project graphs and run algorithms using pure Python code.

Who it’s for

Data scientists and developers who work with graph databases and need to perform large-scale graph analysis, implement machine learning on graphs, or build custom graph algorithms.

Highlights

  • Integrated Analytics: Runs graph algorithms and ML pipelines directly via Cypher procedures in Neo4j.
  • Python Integration: Includes a dedicated Python client for a more flexible development experience.
  • Custom Algorithm Support: Offers a Pregel API for creating bespoke graph algorithms.
  • OpenGDS: Provides an open-source version of the library under the GNU Public License v3.0.

Related

  • Project
    neo4j/neo4j-graphrag-python

    The official Neo4j Python package for building GraphRAG applications, enabling the automated construction of knowledge graphs and hybrid retrieval for LLMs.

  • Project
    neo4j-labs/llm-graph-builder

    A tool that uses LLMs and LangChain to transform unstructured data from various sources into structured Knowledge Graphs stored in Neo4j.

  • Project
    JuliaGraphs/GraphNeuralNetworks.jl

    A collection of Julia libraries for deep learning on graphs, providing graph convolutional layers and data structures for Flux and Lux frameworks.

  • Project
    rapidsai/cugraph

    A collection of GPU-accelerated graph analytics packages that enable the creation and manipulation of graphs and the execution of scalable graph algorithms.

  • Project
    c2g-dev/city2graph

    A Python library that converts geospatial urban data into spatial and heterogeneous graphs for network analysis and Graph Neural Networks.