v1.1

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