Here is the github repository for 'A Benchmark for Directed Graph Representation Learning in Hardware Designs'
For more details, please refer to our documents.
The combinations of 1) GNN backbones/ Graph Transformers, 2) message passing direction, 3) and Magnetic Laplacian Positional Encoding:
GNN backbones/ Graph Transformers
Message Passing Direction
undirected(-) , directed (DI), and bidirected (BI)
Magnetic Laplacian Positional Encoding
node PE (NPE), and edge PE (EPE)
Datasets Includes: