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
Topological link prediction - these algorithms determine the closeness of pairs of nodes
Node Embeddings - these algorithms compute vector representations of nodes in a graph.
Node Classification - this algorithm uses machine learning to predict the classification of nodes.
Link prediction - these algorithms use machine learning to predict new links between pairs of nodes
There are several ways to get started with graph algorithms:
No download required. Start using Neo4j Graph Algorithms within seconds through a built-in guide and dataset.
- NEuler Graph Data Science Playground
No-code 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 hands-on in the Data Science and Applied Graph Algorithms courses
The following guides provide hands on examples of the different algorithms in the Graph Data Science Library.
The recipes show how to use the Graph Data Science Library to solve common problems.
The following are useful resources once you’ve got a bit of experience with Graph Data Science.
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