Seeds of Science, Visuals in Bloom: Introudcing Whole-Graph Projections
Blog post from Neo4j
Bloom, part of Neo4j’s suite of user tools, has expanded its capabilities to allow the execution of graph algorithms on the entire database, rather than just the current Bloom Scene, offering a more comprehensive view of data insights. This update enables users to run centrality and community detection algorithms across the entire database to uncover vital connections and groupings, enhancing the utility of graph algorithms in data analysis. Bloom provides a no-code interface for configuring and running these algorithms, allowing users to apply rule-based styling to visualize outcomes. With options to run algorithms on either a local scene or the entire graph, Bloom offers flexibility in data exploration, storing results locally or writing them back to the database for future reference. This functionality is available through Neo4j Instances that support the Graph Data Science plugin or the Aura Graph Analytics service, making it accessible without the need for coding. Users can initiate algorithm runs directly from the user interface while also having opportunities to fine-tune and schedule them outside Bloom using other Neo4j tools.
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