Neo4j Graph Data Science
Graph Data Science is an analytics and machine learning (ML) solution that analyzes relationships in data to improve predictions and discover insights. It plugs into data ecosystems so data science teams can get more projects into production and share business insights quickly.
Why Neo4j Graph Data Science?
Graph structure makes it possible to explore billions of data points in seconds and identify hidden relationships that help improve predictions. Our library of opens in new tabgraph algorithms, ML modeling, and opens in new tabvisualizations help your teams answer questions like what's important, what's unusual, and what's next.
Built for Data Scientists
Use a Python environment to discover insights and quickly demonstrate value from graph analysis. Data science teams can start experimenting quickly and get more projects completed with support from pre-configured graph algorithms and automated procedures.
Highlights: native Python client, automated ML pipelines, and model sharing and reuse.
Make Better Predictions
Answer questions with graph-based queries, search, and pathfinding. Further your analysis and inference through a broad set of graph algorithms from centrality to node embedding and conduct graph-native unsupervised and supervised ML for clustering, similarity, classification, and opens in new tabmore.
Highlights: catalog of 65+ graph algorithms, graph native ML pipelines, graph visualization tools.
opens in new tabAnswer big questions with our graph algorithms
Data Ecosystem Integration
Seamlessly access, store, move, and share data with 30+ connectors and extensions. Fast and scalable import/export allows you to bring in data from any source and to integrate with other data science and ML libraries, data platforms, and pipelines. Connect to your choice of BI tool.
Highlights: Apache Spark connector, Apache Arrow integration, Data Warehouse connector.
Move Projects into Production
Get projects adopted and save time on infrastructure, configuration, and administration. Use native capabilities to launch models and workflows or integrate algorithm results with external ML pipelines. Quickly and seamlessly release workflows with our included enterprise database or database of your choice.
Highlights: Deploy graph native ML pipelines, integrate with Google Vertex AI, Amazon Sagemaker, ML and AI pipelines.
Do you manage data science teams?
opens in new tabRead our Manager's Guide to Graph Data Science
The Business Impact of Data Science Teams
Data science teams help answer complex operational questions to drive organizational success. These insights inform changes in strategies and reveal high-performing areas. Visualization tools help business stakeholders easily understand these connected data relationships.
Anomaly and Fraud Detection
Analyze relationships and behaviors to detect fraud and anomalies across banking, insurance, government programs, and other industries.
Recommendation Engines
Build stronger recommendation engines based on similar user profiles, behaviors, preferences, and past online activity.
Route Optimization
Manage supply chain inefficiencies by calculating what-if scenarios and predict future issues with pathfinding algorithms.
Customer 360°
Improve knowledge across customers, partners, and employees for a 360 view. Unify these entities for marketing, payments, usage, and more.
Enhance Your Graph Data Science Experience
Get expert technical support and a vibrant community.
Case Studies
Learn how our clients solve their toughest data challenges with graph technology.
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"Neo4j Graph Data Science is a great tool because we can tweak our models over time to improve them. We have everything we need all in one place with Graph Data Science - it makes it easy for us to focus on building our business because the software works easily with our existing toolset and data science approaches."
Daniel Brady, CEO, Orita
