pyTigerGraph GDS Module

The centerpiece of the ML Workbench is its Python client: pyTigerGraph.

This Python package contains all the essential utilities you need to bootstrap your Graph Machine Learning journey with these key functions:

  • Data loading/export

  • Graph partitioning for preparing your training, validation, and test data set for your supervised graph machine learning model

  • Featurizer for generating unique graph features

  • Subgraph sampling for your stochastic training process.

Please refer to the GDS Functions section of the pyTigerGraph documentation to learn more about the submodules, classes, and methods, and to see working examples and code snippets.