Add GTVConv and AsymCheegerCutPool layers Bump minimum supported Python version to 3.7 Add automatic handling of Tensorflow installation when installing Spektral on Apple Silicon Various bugfixes

v1.2

This release brings some new features and improvements

New features

• New convolutional layer CensNetConv
• New batch-mode version of GINCov
• New pooling layers: JustBalancePool and DmonPool
• New datasets: DBLP and Flickr

Compatibility changes

• Python 3.6 is no longer supported officially

API changes

• XENetDenseConv is now called XENetConvBatch

Bugfixes

• Fix crash when using Disjoint2Batch and improve the performance of the layer
• Fix minor bug that would block kwargs forwarding in SRC layers (only affects custom layers, not the ones in the library)
• Fix preprocess method in DiffusionConv

v1.1

This release mostly introduces the new Select, Reduce, Connect API for pooling layers and a bunch of features, improvements, and bugfixes from previous patches.

Most of the new features are backward compatible with two notable exceptions:

• pooling layers must be ported to the new SRC interface. See the documentation for more details.
• Custom MessagePassing layers that used get_i and get_j must be updated to use get_targets and get_sources. This only affects you if you have a custom implementation based on the MessagePassing class, otherwise the change will be transparent.

This version of Spektral supports Python >=3.6 and up, and TensorFlow >=2.2.

New features

• New general class for pooling methods based on the Select, Reduce, Connect framework (https://arxiv.org/abs/2110.05292)
• Node-level labels support to BatchLoader
• New GCN model
• GNNExplainer model
• XENetConv convolutional layer
• LaPool pooling layer
• GATConv now supports weighted adjacency matrices

Compatibility changes

• Update minimum supported Python version to 3.6
• Update minimum supported TensorFlow version to 2.2

API changes

• Remove channels argument from CrystalConv (output must be the same size as input)
• All pooling layers are now based on SRC and have a unified interface. See docs for more details (migration from the old layers should be straightforward by changing relevant keyword arguments)
• Rename "i" and "j" with "targets" and "sources" in the MessagePassing-based classes

Bugfixes

• Fix bug in GlobalAttnSumPool that caused the readout to apply attention to the full disjoint batch
• Fixed parsing of QM9 to return the full 19-dimensional labels

Other

• Minor fixes in examples
• GCN/GAT examples are now more consistent with the original papers

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.