Graph Classification with Transformers

Transformer architectures like Graphormer and GraphGPS now handle graph-level classification without message-passing by tokenizing nodes and edges into standard attention sequences. They match GNN accuracy on OGB benchmarks without custom aggregation layers.

For fraud detection or molecular property pipelines built on PyTorch Geometric, this means a fine-tuned graph transformer replaces hand-engineered message-passing stacks. Most teams are still writing custom GNN layers when pre-trained graph transformers generalize better with half the code. Designing bespoke GNN architectures for graph classification in 2026 is just delayed adoption.