Use Cases: Neo4j for Graph Data Science
Today’s businesses are faced with extremely complex challenges and opportunities that require more flexible, intelligent approaches.
That’s why Neo4j created the first enterprise graph framework for data scientists – to improve predictions that drive better decisions and innovation.
Neo4j for Graph Data Science™ incorporates the predictive power of relationships and network structures in existing data to answer previously intractable questions and increase prediction accuracy.
Neo4j for Enterprise Graph Data Science
From pointers to patterns to predictions, only Neo4j offers such breadth and depth of advanced graph analytics and data science capabilities in an integrated enterprise environment.
Our efficient property graph model stores nodes and their corresponding relationships together, so you just follow the pointers for real-time queries. The Neo4j graph algorithms inspect global structures to find important patterns and now, with graph embeddings and graph database machine learning training inside of the analytics workspace, we can make predictions about your graph.
Neo4j for Graph Data Science is comprised of the following products:
A toolkit with a flexible data structure for analytics and a library with five varieties of powerful graph algorithms.
A highly scalable, native graph database, purpose built to persist and protect relationships.
A graph visualization and exploration tool that allows users to visualize algorithm results and find patterns using codeless search.
Graph Data Science helps businesses across industries leverage highly predictive, yet largely underutilized relationships and network structures to answer unwieldy problems.
Examples include user disambiguation across multiple platforms and contacts for more personalized services and marketing, identifying early interventions for complicated patient journeys to improve outcomes, and predicting fraud through sequences of seemingly innocuous behavior.
To accomplish these goals, organizations explore the results of graph algorithms and then use predictive features for further analysis, machine learning or to support AI systems. With this approach, Neo4j customers are demonstrating that graphs bring tremendous value to advanced analytics, machine learning and AI.
Read the white paper, Artificial Intelligence & Graph Technology: Enhancing AI with Context & Connections, on how graph technology enhances machine learning and AI projects by providing context and connections within the underlying data.
Graph Data Science For Dummies
Learn the foundations of graph data science and dive into graph analytics and algorithms that solves real-world problems using machine learning and more.Get the free book
Case Study: NYP Advances Analysis to Track Infections with Neo4j
Learn how NYP Hospital's analytics team used graph data science to relate all their event data, enabling them to track infections and take strategic action to contain them.Read the case study
Neo4j Graph Data Science Sandbox
Test drive Neo4j Bloom and the GDS Library together with our graph data science sandbox – the fastest way to experiment since there's nothing to install or data to load.Try the sandbox
How Graphs Enhance Artificial Intelligence, with Neo4j's Amy Hodler
Amy Hodler, Analytics & AI Program Manager at Neo4j, speaks at GraphTour on how graph technology enhances AI, with tactical steps in how to move forward in graph data science.Watch video
Incorporating the predictive power of relationship in advanced analytics and machine learning enables you to continually improve predictive accuracy.
Answer Intractable Questions
Graph algorithms are a subset of data science algorithms created to analyze network structures so you can better understand complex systems and answer more complicated questions.
Using an industry leader to add graph based features to existing data science pipelines is a low-risk way to put more accurate models into production faster.
Analytics and machine learning requires a lot of data to increase accuracy but most models today aren’t using their existing data about relationships and network structures.
Data science is inherently iterative so it’s essential to use a framework that brings in highly predictive relationships while streamlining the process of moving from data to analysis to visualization and back.
Lack of Scale and Support
Data scientists need enterprise scale, productions features and dedicated data science support that includes packaged and tested algorithms.
Scalable Graph Analytics
Neo4j Graph Data Science library creates a friendly analytics workspace with powerful graph algorithms that can operate over 10’s of billions of nodes and relationships.
Integrated Native Graph Store
Neo4j graph database natively stores interconnected data for persistence and automates data reshaping for analytics.
Intuitive Graph Visualization
Neo4j Bloom enables graph novices and experts to explore results visually, quickly prototype concepts and collaborate with different groups.
Learn More About AI & ML Use Cases
Datanami: Why Knowledge Graphs Are Foundational to Artificial Intelligence