Neo4j Graph Data Science Library
Enterprise Analytics Workspace and Graph-Native Machine Learning
Get your free eBook copy of the new O'Reilly book on Graph Algorithms
Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value – from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions.
Flexible, Scalable Analytics Workspace
Native Graph Storage
For efficiency, the graph algorithms run in a customized analytics workspace created by the graph catalog. The computational graphs are loaded in parallel and materialized in-memory from the Neo4j Graph Database.
The GDS Library automates the data transformations so you can easily benefit from maximum compute performance for analytics as well as native graph storage for compact persistence.
Mutable In-memory Graph
The in-memory computational graph is mutable, which means you can reshape it on the fly and layer analytics steps – all without altering the original graph until you’re ready to save results.
This means data scientists can build workflows to streamline processes, like automatically loading a named graph, chaining algorithms together and ultimately writing to their database or exporting new graphs.
Generate Better Predictions
Alicia Frame, Lead Product Manager and Data Scientist at Neo4j, explained why Neo4j for Graph Data Science is the most expeditious way to generate better predictions.
“A common misconception in data science is that more data increases accuracy and reduces false positives,” explained Frame. “In reality, many data science models overlook the most predictive elements within data – the connections and structures that lie within. Neo4j for Graph Data Science was conceived for this purpose – to improve the predictive accuracy of machine learning, or answer previously unanswerable analytics questions, using the relationships inherent within existing data.”
The Graph Data Science Library is part of the Neo4j Graph Data Science framework built for data scientists. It offers a friendly data science experience with guardrails like logical memory management, intuitive API and extensive documentation.
Data scientists can also visually explore algorithm results with Neo4j Bloom and share visual perspectives across data science, development and business teams for better collaboration.
“A common misconception in data science is that more data increases accuracy and reduces false positives. In reality, many data science models overlook the most predictive elements within data – the connections and structures that lie within.”
Neo4j Data Scientist
The world is driven by connections – it’s time you leveraged the value hidden in your connected data.
To not just react but predict and prescribe the best course of action, you need powerful data science created for connected systems.
The First Enterprise Framework for Graph Data Science
Answer previously intractable questions and use the predictive power of relationships for analytics and machine learning
Scale to tens of billions of nodes with optimized, parallelized algorithms and a compact footprint
Performance of a graph-specific analytics workspace for computation integrated with a native graph database
Scalable in-memory graph model that loads in parallel, flexibly aggregates and reshapes underlying data models
Friendly interface with flexible graph reshaping in-memory, logical guardrails and a graph visualization tool
Production features from the graph leader with dedicated graph data science support
Improving Analytics, ML & AI for Enterprises
- 27 Million warranty & service documents parsed for text to knowledge graph
- Graph is context for AI to learn “prime examples” and anticipate maintenance
- Improves satisfaction and equipment lifespan
- Connecting 50 research databases, 100k’s of Excel workbooks, 30 bio-sample databases
- Bytes 4 Diabetes Award for use of a knowledge graph, graph analytics, and AI
- Customized views for research angles
- Almost 70% of credit card fraud was missed
- About 1 billion nodes and 1 billion relationships to analyze
- Graph analytics with queries & algorithms help find $ millions of fraud in 1st year
Download the White Paper
White Paper: Financial Fraud Detection with Neo4j Graph Data Science
Explore using the Graph Data Science Library and Neo4j Bloom with the white paper, Financial Fraud Detection with Graph Data Science: How Graph Algorithms & Visualization Better Predict Emerging Fraud Patterns, and learn how to tap into the power of graph technology for higher quality predictions.