Happy anniversary, Neo4j Graph Data Science!
Has it really been a year already?!
Happy anniversary, Neo4j Graph Data Science! Has it really been a year already?! We announced the Neo4j’s Graph Data Science (GDS) framework back in April 2020, and we’ve come a long way since!
To Catch You Up
The GDS Library started with 42 graph algorithms in five categories: pathfinding, community detection, centrality, similarity, and heuristic link prediction. It also included an analytics workspace to transform your Neo4j database into an in-memory format specifically to run analytics workloads.
So, What’s New?
We added algorithms (nearly 60 now) based on your feedback, like Hyperlink-Induced Topic Search (HITS) and Speaker Listener Label Propagation, as well as a Pregel API so you could write your own algorithms. We’re really excited to be adding some community contributions soon, as well! However, most of our time this last year has been focused on three areas:
- New graph-native ML capabilities based on state-of-the science ML techniques to improve your predictions, even when you don’t know exactly what you’re looking for.
- Optimizing the infrastructure for even larger workloads and mature enterprise processes.
- Tighter integration with the Neo4j database and platform to improve workflows.
To get your hands on the latest from the GDS Library, visit our download center or go straight to our GitHub repository. To get started check out the Neo4j graph data science sandbox and test drive the GDS Library and Bloom together.
|Read our blog for the details!|
Alicia and Amy
Sponsored by Neo4j