v1.2.1

[1.2.1] - 2022-11-09

Release of pyTigerGraph version 1.2.1.

Fixed:

• Error handling in visualization module
• Error handling FastRP in featurizer
• Fixed unit tests.

v1.2

[1.2] - 2022-11-09

Release of pyTigerGraph version 1.2.

Added:

• The Datasets class, a way to easily import standard datasets into a database instance.
• The visualizeSchema function to visualize graph schemas.
• Proper deprecation warnings.
• Logging capabilities using native Python logging tools.
• Ability to run asynchronous queries from runInstalledQuery()

Changed:

• Many changes to the featurizer capability, including:
  • Automatically selecting the correct version of a graph data science algorithm given your version of the database.
  • Automatically creating the schema change necessary to run the algorithm and store the results to an attribute.
  • If the algorithm is not already installed at runtime, and is included in the TigerGraph Graph Data Science Library, the algorithm will be installed automatically.
  • Adding more supported algorithms, in categories such as similarity and topological link prediction.

v1.1

[1.1] - 2022-09-06

Release of pyTigerGraph version 1.1.

Added:

• TensorFlow support for homogeneous GNNs via the Spektral library.
• Heterogeneous Graph Dataloading support for DGL.
• Support of lists of strings in dataloaders.

Changed:

• Fixed KeyError when creating a data loader on a graph where PrimaryIdAsAttribute is False.
• Error catch if Kafka dataloader doesn't run in async mode.
• Refresh schema during dataloader instantiation and featurizer attribute addition.
• Reduce connection instantiation time.
• Reinstall query if it is disabled.
• Confirm Kafka topic is created before subscription.
• More efficient use of Kafka resources.
• Allow multiple consumers on the same data.
• Improved deprecation warnings.

v1.0.2

[1.0.2] - 2022-08-03

Bug Fixes:

• Error catch if Kafka dataloader doesn't run in async mode.
• Refresh schema during dataloader instantiation.
• Reduce connection instantiation time.

v1.0.1

[1.0.1] - 2022-07-12

Changed:

• Fixed KeyError when creating a data loader on a graph where PrimaryIdAsAttribute is False.

Version 1.0

[1.0] - 2022-07-11

Release of pyTigerGraph version 1.0, in conjunction with version 1.0 of the TigerGraph Machine Learning Workbench.

Added:

• Kafka authentication support for ML Workbench enterprise users.
• Custom query support for Featurizer, allowing developers to generate their own graph-based features as well as use our built-in Graph Data Science algorithms.

Changed:

• Additional testing of GDS functionality
• More demos and tutorials for TigerGraph ML Workbench, found here.
• Various bug fixes.

Version 0.9.2

[0.9.2]

Changed:

• Authentication with TigerGraph Cloud instances. Added gsqlSecret to replace username and password parameters when connecting to a TigerGraph Cloud instance provisioned after July 5th, 2022.

Version 0.9.1

[0.9.1] - 2022-06-21

Added new parameter, tgCloud for when connecting to a TigerGraph Cloud instance. Set to True if using a new TigerGraph Cloud

Changed

• Deprecated gcp parameter, as tgCloud supercedes this. Existing code will be compatible.

Version 0.9

[0.9] - 2022-05-16

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. While becoming an official product, we are committed to keeping pyTigerGraph true to its roots as an open-source project. Check out the contributing page and GitHub issues if you want to help with pyTigerGraph’s development.

Changed

• Feature: Include Graph Data Science Capability

  • Many new capabilities added for graph data science and graph machine learning. Highlights include data loaders for training Graph Neural Networks in DGL and PyTorch Geometric, a "featurizer" to generate graph-based features for machine learning, and utilities to support those activities.

• Documentation: 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.

• Testing: There is now well-defined testing for every function in the package. A more defined testing framework is coming soon.

• Code Structure: A major refactor of the codebase was performed. No breaking changes were made to accomplish this.