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TypeDB-ML

Previously known as KGLIB.

TypeDB-ML provides tools to enable graph algorithms and machine learning with TypeDB.

There are integrations for NetworkX and for PyTorch Geometric (PyG).

NetworkX integration allows you to use a large library of algorithms over graph data exported from TypeDB.

PyTorch Geometric (PyG) integration gives you a toolbox to build Graph Neural Networks (GNNs) for your TypeDB data, with an example included for link prediction (or: binary relation prediction, in TypeDB terms). The structure of the GNNs are totally customisable, with network components for popular topics such as graph attention and graph transformers built-in.

Features

NetworkX

PyTorch Geometric

Other

Resources

You may find the following resources useful, particularly to understand why TypeDB-ML started:

Quickstart

Install

Run the Example

Take a look at the PyTorch Geometric heterogeneous link prediction example to see how to use TypeDB-ML to build a GNN on TypeDB data.

Development

To follow the development conversation, please join the Vaticle Discord, and join the #typedb-ml channel. Alternatively, start a new topic on the Vaticle Discussion Forum.

TypeDB-ML requires that you have migrated your data into a TypeDB or TypeDB Cluster instance. There is an official examples repo for how to go about this, and information available on migration in the docs. Alternatively, there are fantastic community-led projects growing in the TypeDB OSI to facilitate fast and easy data loading, for example TypeDB Loader.

Building from Source

It's expected that you will use Pip to install, but should you need to make your own changes to the library, and import it into your project, you can build from source as follows:

Clone TypeDB-ML:

git clone git@github.com:vaticle/typedb-ml.git

Go into the project directory:

cd typedb-ml

Build all targets:

bazel build //...

Run all tests. Requires Python 3.7+ on your PATH. Test dependencies are for Linux since that is the CI environment:

bazel test //typedb_ml/... --test_output=streamed --spawn_strategy=standalone --action_env=PATH

Build the pip distribution. Outputs to bazel-bin:

bazel build //:assemble-pip