pyTigerGraph Module Reference

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.

This reference describes the submodules, classes, and methods that are available within the pyTigerGraph package. Please refer to the official pyTigerGraph documentation under the section "GDS Functions" to learn more about each of these capabilities with working examples and code snippets: GDS Functions