v1.3

[1.3] - 2023-02-09

Release of pyTigerGraph version 1.3.

Added:

• Two new dataloaders: HGTLoader and NodePiece
  • The HGTLoader is a dataloader that performs stratified neighbor sampling Heterogeneous Graph Transformer.
  • NodePiece is a scalable graph embedding approach introduced in NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
• Callback functions to all dataloaders: users can write functions to process the batch in a background thread before it is passed into the training loop.
• A delimiter parameter to all dataloaders: users can choose what delimiter they want to separate attributes they are loading from the graph.

Changed:

• Added better error messaging when REST requests are incorrect.
• Template query support in the featurizer (requires TigerGraph Database version 3.9+)
• Data splitters automatically perform a schema change if needed to add attributes to the database.

Fixed:

• Fixed how custom SSL certificates were handled when instantiating the GSQL client.

v1.2.6

[1.2.6] - 2022-11-30

Release of pyTigerGraph version 1.2.6.

Fixed:

• Fix dependency error importing tqdm for datasets functionality. Will be automatically downloaded if installing the GDS variant of pyTigerGraph, else it will try and throw a warning if tqdm is not available.

v1.2.5

[1.2.5] - 2022-11-15

Release of pyTigerGraph version 1.2.5.

Fixed:

• Fix featurizer install of FastRP on versions DB versions 3.8+ with global schema elements.

v1.2.4

[1.2.4] - 2022-11-14

Release of pyTigerGraph version 1.2.4.

Changed:

• Improve data ingestion print out
• Skip downloading if dataset exists on disk

Fixed:

• Fix metrics issue
• Fix unit test issue in featurizer

v1.2.3

[1.2.3] - 2022-11-13

Release of pyTigerGraph version 1.2.3.

Fixed:

• NeighborLoader fetch() functionality restored.
• runInstalledQuery() defaulting back to GET REST requests.
  • If you wish to pass empty sets, set the usePost = True parameter.

v1.2.2

[1.2.2] - 2022-11-11

Release of pyTigerGraph version 1.2.2.

Fixed:

• The way empty sets are serialized into JSON for installed queries.

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.