neo4j/graph-data-science-client
A Python client for the Neo4j Graph Data Science (GDS) library
This tool helps data scientists and machine learning engineers analyze complex relationships in their data using Neo4j's Graph Data Science (GDS) library. You can input raw graph data, run powerful graph algorithms like PageRank, and build machine learning models like node classification pipelines to understand connections and predict outcomes. It's designed for professionals working with large, interconnected datasets.
236 stars.
Use this if you need to run advanced graph algorithms or build machine learning models directly on your Neo4j graph databases using Python.
Not ideal if you are looking for simple data visualization or basic querying of a graph database without needing advanced analytical or machine learning capabilities.
Stars
236
Forks
56
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 11, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/neo4j/graph-data-science-client"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
a-r-j/graphein
Protein Graph Library
raamana/graynet
Subject-wise networks from structural MRI, both vertex- and voxel-wise features (thickness, GM...
pykale/pykale
Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for...
dmlc/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.