Relational Graph Attention Networks

This contribution relates to bringing attention to heterogeneous (relational) graphs and incorporating this relation-aware attention operator into PyTorch Geometric Library.

Offers two different mechanisms to compute attention for relational graphs, i.e. within-relation and across-relation.
This implementation also provides four different cardinality preservation options (additive, scaled, f-additive, and f-scaled) to further improve attention computation for the heterogeneous graphs.
Wrote custom unit tests to verify the technique’s accuracy.
Libraries/Framework: torch-scatter, torch-sparse, PyTorch, PyTorch Geometric, and PyTest