v1.0.0: public beta

🎉 We are excited to mark this release as the first public version for our spectral GNN framework! This release solidifies the code arrangement of:

• benchmark/: codes for benchmark experiments.
• pyg_spectral/: core codes for spectral GNNs designs.
  • nn.conv: spectral spectral filters.
  • nn.models: common neural network architectures.

Currently, the framework supports two training schemes:

• Full-batch: available to Iterative and Decoupled models
• Mini-batch: available to Precomputed models

Milestone May 23

• Refactor model/conv layer division
• New filters
• Experiments to produce results

Milestone April 4

Basic pipeline, filter implementation, degree-specific evaluation