GRAPH DATA SCIENCE LIBRARY

Obtain Insights at Scale with Graph Algorithms for Machine Learning

Do you sometimes feel limited by an inability to leverage the potential of AI in uncovering insights hidden in your connected data?

Graph algorithms are essential building blocks for analyzing your connected data and for AI methods which gain deeper insights from that data. Graph algorithms can be used directly as unsupervised learning, to enrich training sets for supervised learning, or to perform ML/AI itself.

TigerGraph’s in-database data science algorithms improve your analytics and machine learning capabilities.

Fast, Scalable, Open-Source and In-Database Graph Data Science Library

Over 50 algorithms that span Dependencies, Clustering, Similarity, Matching / Patterns, Flow, Centrality, and Search

Practical and Scalable Graph Embedding Algorithms

Improve similarity, classification, and link prediction tasks via embedding algorithms which transforming a graph’s neighborhood into a compact vector 

Coming soon: Integrated Graph+ML Workbench for Cloud and On-Premise Users