Using Graph Data Science
Neo4j’s Graph Data Science (GDS) library implements a lot of graph algorithms to help users derive intelligence from data. They are implemented to run in parallel, allowing algorithms to run fast and provide results quickly.
The algorithms included are as follows:
- Node centrality algorithm
- Community detection algorithm
- Similarity algorithms including the Jaccard, Cosine, Pearson, Euclidean and k-nearest neighbor algorithms
- Path-finding algorithms including the Dijkstra, A* shortest path, Yen’s shortest path, breadth-first, depth-first, and random walk algorithms
- Node embedding algorithms including FastRP, GraphSAGE, and Node2Vec
- Link prediction algorithms
Along with these graph algorithms, this library also provides the following machine learning pipelines:
- Node classifications
- Link predictions
This library is a must-have tool in any data scientist’s toolkit to process graph data...
