Neo4j, a provider of graph technology, is launching Neo4j for Graph Data Science, a data science environment built to harness the predictive power of relationships for enterprise deployments.
Neo4j for Graph Data Science helps data scientists leverage highly predictive, yet largely underutilized relationships and network structures to answer unwieldy problems.
Examples include user disambiguation across multiple platforms and contact points, identifying early interventions for complicated patient journeys and predicting fraud through sequences of seemingly innocuous behavior.
Neo4j for Graph Data Science combines a native graph analytics workspace and graph database with scalable graph algorithms and graph visualization for a reliable, easy-to-use experience.
This framework enables data scientists to confidently operationalize better analytics and machine learning models that infer behavior based on connected data and network structures.
Neo4j for Graph Data Science enables data scientists to answer questions that are only addressable through understanding relationships and data structures.
Graph algorithms are a subset of data science tools that capitalize on network structure to infer meaning and make predictions such as:
- Cluster and neighbor identification through community detection and similarity algorithms
- Influencer identification through centrality algorithms
- Topological pattern matching through pathfinding and link prediction algorithms
With Neo4j for Graph Data Science, teams deploy a proven solution at massive scale to run optimized graph algorithms over tens of billions of nodes with production features such as deterministic seeding, which provides starter values and consistent results for reproducible machine learning workflows.
Through intelligent integration of network analytics and a database, Neo4j automates data transformations so users get maximum compute performance for analytics and native graph storage for persistence.
For more inormtion about this release, visit https://neo4j.com/.