We are excited to announce the pyTigerGraph v0.9 release! This release adds many new features for graph machine learning and graph data science, a refactoring of core code, and more robust testing. Additionally, we have officially “graduated” it to an official TigerGraph product. This means brand-new documentation, a new GitHub repository, and future feature enhancements.
pyTigerGraph 0.9 was released on May 16th, 2022.
Many new capabilities added for graph data science and graph machine learning.
Data loaders for training Graph Neural Networks in DGL and PyTorch Geometric
A "featurizer" to generate graph-based features
Metric trackers for precision, recall, and accuracy
Vertex and edge splitters for generation of train/validation/testing splits.
We have moved the documentation to the official TigerGraph Documentation site and updated many of the contents with type hints and more descriptive parameter explanations.
There is now well-defined testing for every function in the package. A more defined testing framework is coming soon.