/SAN

Primary LanguagePythonMIT LicenseMIT

SAN

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

full_method

Overview

  • 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.