Online Course Introduction to Graph Algorithms with Neo4j 4.0 Overview of Graph Algorithms Introduction to Graph Data Science library Graph Algorithms Workflow Environment Setup Graph Management Community Detection Algorithms Centrality Algorithms Similarity Algorithms Recipes Analysis Memory Requirements Estimation Additional Information… Read more →

Introduction to Graph Data Science library

Neo4j for Graph Data Science

Neo4j for Graph Data Science is a toolbox that combines a native graph analytics workspace and graph database with scalable graph algorithms and graph visualization for a reliable, easy-to-use experience. This framework enables data scientists to confidently operationalize better analytics and machine learning models that infer behavior based on connected data and network structures.

Neo4j for graph data science


Graph data science steps

Neo4j Database

Neo4j is a native graph database, built from the ground up to leverage not only data but also data relationships. Neo4j connects data as it’s stored, enabling queries never before imagined, at speeds never thought possible.

Neo4j Bloom

Neo4j Bloom is an easy-to-use graph exploration application for visually interacting with Neo4j graphs.

Bloom gives graph novices and experts the ability to visually investigate and explore their graph data from different business perspectives. Its illustrative, codeless search-to-visualization design makes it the ideal interface for fostering communication between peers, managers and executives, and share the innovative work of their graph development and analytics teams.

Graph Data Science library

The Neo4j Graph Data Science (GDS) library contains many graph algorithms. The algorithms are divided into categories which represent different problem classes. The categories are listed in this chapter.

Algorithms exist in one of three tiers of maturity:


Indicates that the algorithm has been tested with regards to stability and scalability. Algorithms in this tier are prefixed with gds.<algorithm>.


Indicates that the algorithm is a candidate for the production-quality tier. Algorithms in this tier are prefixed with gds.beta.<algorithm>.


Indicates that the algorithm is experimental and might be changed or removed at any time. Algorithms in this tier are prefixed with gds.alpha.<algorithm>.

At the moment the the GDS library contains

Graph data science algorithms



In this chapter you have familiarized yourself with the Neo4j for Graph Data Science toolkit.

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