Implementation of Spectral Attention Networks, a powerful GNN that leverages key principles from spectral graph theory to enable full graph attention.

• nets contains the Node, Edge and no LPE architectures implemented with PyTorch.
• layers contains the multi-headed attention employed by the Main Graph Transformer implemented in DGL.
• train contains methods to train the models.
• data contains dataset classes and various methods used in precomputation.
• configs contains the various parameters used in the ablation and SOTA comparison studies.
• misc contains scripts from https://github.com/graphdeeplearning/graphtransformer to download datasets and setup environments.
• scripts contains scripts to reproduce ablation and SOTA comparison results. See scripts/reproduce.md for details.