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 Library (GDSL)
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.
Amy Hodler and Alicia Frame also explain more about the library and share hands on examples in this talk from the Connections: Graph Data Science event.
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.
Getting Started
There are several ways to get started with graph algorithms:
 Sandbox

No download required. Start using Neo4j Graph Algorithms within seconds through a builtin guide and dataset.
 NEuler Graph Data Science Playground

Nocode graph algorithms using this Graph App that provides a UI on top of the Graph Data Science Library.
 Free online training

Learn how to use graph algorithms handson in the Data Science and Applied Graph Algorithms courses
Tutorials
The following guides provide hands on examples of the different algorithms in the Graph Data Science Library.
Explanation
The following guides provide more details and background for various parts of the Graph Data Science Library.
Additional Resources
The following are useful resources once you’ve got a bit of experience with Graph Data Science.
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