v1.0

The 1.0 release of Spektral is an important milestone for the library and brings many new features and improvements.

If you have already used Spektral in your projects, the only major change that you need to be aware of is the new datasets API.

This is a summary of the new features and changes:

• The new Graph and Dataset containers standardize how Spektral handles data.
  This does not impact your models, but makes it easier to use your data in Spektral.
• The new Loader class hides away all the complexity of creating graph batches.
  Whether you want to write a custom training loop or use Keras' famous model-dot-fit approach, you only need to worry about the training logic and not the data.
• The new transforms module implements a wide variety of common operations on graphs, that you can now apply() to your datasets.
• The new GeneralConv and GeneralGNN classes let you build models that are, well... general. Using state-of-the-art results from recent literature means that you don't need to worry about which layers or architecture to choose. The defaults will work well everywhere.
• New datasets: QM7 and ModelNet10/40, and a new wrapper for OGB datasets.
• Major clean-up of the library's structure and dependencies.
• New examples and tutorials.