graph-transformers

Quartet introduces a graph transformer architecture with quad-branch cross-attention and a causal random walk sampler to enhance performance on relational graph tasks, outperforming state-of-the-art baselines like RelGT and HGT.

PatchSTG introduces a patch-based spatiotemporal graph Transformer for traffic forecasting on irregular sensor networks, achieving near-linear complexity while maintaining competitive performance.