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Neo4j Graph Algorithms
Graph algorithms provide one of the most potent approaches to analyzing connected data because their mathematical calculations are specifically built to operate on relationships. They describe steps to be taken to process a graph to discover its general qualities or specific quantities.
Neo4j Graph Data Science is a library that provides efficiently implemented, parallel versions of common graph algorithms for Neo4j 3.x and Neo4j 4.x exposed as Cypher procedures. It forms the core part of your Graph Data Science platform.
The library contains implementations for the following types of algorithms:
- Path Finding – these algorithms help find the shortest path or evaluate the availability and quality of routes
- Centrality – these algorithms determine the importance of distinct nodes in a network
- Community Detection – these algorithms evaluate how a group is clustered or partitioned, as well as its tendency to strengthen or break apart
- Similarity – these algorithms help calculate the similarity of nodes
- Link Prediction – these algorithms determine the closeness of pairs of nodes
- Node Embeddings – these algorithms compute vector representations of nodes in a graph.
There are several ways to get started with graph algorithms:
- Sandbox – No download required. Start using Neo4j Graph Algorithms within seconds through a built-in guide and dataset.
- Graph Algorithms Playground – execute graph algorithms without any code using this Graph App that provides a UI on top of the Graph Algorithms Library.
- Free online training – learn how to use graph algorithms hands-on in the Data Science and Applied Graph Algorithms courses
- Docs – dive straight in with the Neo4j Graph Data Science User Guide.
The following guides provide more details and background for various parts of the Graph Data Science Library.
The following are useful resources once you’ve got a bit of experience with Graph Data Science.