TigerGraph Machine Learning Workbench: A Better Way To Develop Graph-based Machine Learning Models

Recent research has shown the success of graph neural networks (GNNs) and graph-enhanced machine learning models outperforming conventional machine learning approaches. TigerGraph’s Machine Learning Workbench provides a faster and easier way to develop graph-enhanced machine learning models and GNNs by utilizing the connected data stored in a TigerGraph database. In this session, we'll introduce the TigerGraph Machine Learning Workbench, showing how simple it is to create a GNN model for a fraud transaction detection application.


  • Andrew Wei, Product Manager at TigerGraph
  • Nikita Iserson, Solution Engineer at TigerGraph