About the Graph Data Science category

This catagory contains questions and discussions around graph algorithms and TigerGraph’s Machine Learning (ML) Workbench.

Graph Algorithms Overview

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.

:page_with_curl: Docs
:tv: Graph Data Science Video

TigerGraph’s Machine Learning (ML) Workbench Overview

TigerGraph’s Machine Learning (ML) Workbench is a Jupyter-based Python development framework that enables data scientists to quickly build powerful deep learning AI models using connected data. Due to its accurate predictive power, the ML Workbench enables organizations to unlock even better insights and greater business impact on node prediction applications such as anti-money laundering or edge prediction applications such as product recommendations.

:page_with_curl: Getting started with ML Workbench
:tv: Machine Learning Workbench Overview Video