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Exphormer: Scaling transformers for graph-structured data

Google just solved the graph transformer bottleneck. Exphormer scales to 170K-node datasets—10x larger than before.

Reporting by Google Research Blog 1 min brief
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The KeyNews take

Why it matters

Google Research has released Exphormer, a sparse attention framework that eliminates the quadratic computational bottleneck limiting graph transformers to small datasets. This breakthrough enables transformers to scale to graphs with 170K+ nodes, opening new applications in molecular modeling, social networks, and citation analysis.

The key facts

9 to know

  1. Exphormer scales to 170K-node graphs (ogbn-arxiv dataset with 1.1M edges)

  2. Previous graph transformer limit: few thousand nodes

  3. State-of-the-art on 4 of 5 Long Range Graph Benchmark datasets at publication

  4. Uses d-regular expander graphs to reduce interaction graph edges from O(n²) to O(n)

  5. Presented at ICML 2023

  6. Implementation available on GitHub

  7. PascalVOC-SP F1 score: 0.398 vs 0.375 (standard GraphGPS)

  8. COCO-SP F1 score: 0.346 vs 0.341

  9. PCQM-Contact MRR: 0.364 vs 0.334

Go to the source

Google Research Blogblog.research.google

Publisher excerpt: Posted by Ameya Velingker, Research Scientist, Google Research, and Balaji Venkatachalam, Software Engineer, Google Graphs, in which objects and their relations are represented as nodes (or vertices) and edges (or links) between pairs of nodes, are ubiquitous in computing and machine learning (ML).…
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