StellarGraph is an open-source library implementing a variety of state-of-the-art graph machine learning algorithms. The project is delivered as part of CSIRO’s Data61.

We are happy to announce the 0.8.1 release of the library, which extends StellarGraph capability by adding new algorithms and demos, enhancing interpretability via saliency maps for Graph Attention (GAT), and further simplifying graph machine learning workflows through standardised model APIs and arguments.

This release, we’ve dealt with some bugs from the previous release and introduced new features and enhancements. Some of these include:

• New directed GraphSAGE algorithm (a generalisation of GraphSAGE to directed graphs)
• New Attri2vec algorithm
• New PPNP and APPNP algorithms
• New Graph Attention (GAT) saliency maps for interpreting node classification with Graph Attention Networks
• Added directed SampledBFS walks on directed graphs
• Unified API of GCN, GAT, GraphSAGE, and HinSAGE classes by adding build() method to GCN and GAT classes
• Enhanced unsupervised GraphSage speed up via multithreading
• Support of sparse generators in the GCN saliency map implementation.
• Unified activations and regularisation for GraphSAGE, HinSAGE, GCN and GAT
• Changed from using keras to tensorflow.keras

We’ve also added new demos using real-world datasets to show how StellarGraph can solve these tasks.

Access the StellarGraph project and explore the new features on GitHub. StellarGraph is a Python 3 library.

We welcome your feedback and contributions.

With thanks, the StellarGraph team.