Graph Data Science Library
The Neo4j Graph Data Science Library uses the graph structure in data to help data scientists address complex questions about system dynamics and group behavior.
Businesses use these insights to make valuable predictions, such as pinpointing interactions that indicate fraud, identifying similar entities or individuals, finding the most influential elements in patient or customer journeys, and how to minimize the impact of IT, phone or other network outages.
There are many ways to deploy Neo4j today: on-premise server installation, self-hosted in the cloud with pre-built images, or by simply using Aura, the zero-admin, always-on graph database for cloud developers.
The GDS Library makes addressing these questions feasible. Data scientists benefit from a customized, flexible data structure for global computations and a repository of powerful, robust graph algorithms to quickly compute results over tens of billions of nodes.
Graph algorithms provide unsupervised machine learning (ML) methods and heuristics that learn and describe the topology of your graph. The GDS Library includes hardened graph algorithms with enterprise features, like deterministic seeding for consistent results and reproducible ML workflows.