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arXiv Machine Learning By Javier Porras-Valenzuela, Zhiyang Wang, Teresa Shang, Yusu Wang, Alejandro Ribeiro

Size-Transferability of Graph Transformers with Convolutional Positional Encodings

Read the original on arXiv Machine Learning →

The paper investigates Graph Transformers (GTs) that use Graph Neural Network (GNN)-based positional encodings. By analyzing GTs through manifold limit models, the authors establish a theoretical link between GTs and Manifold Neural Networks (MNNs), showing that GTs inherit transferability guarantees from their positional encodings. Experiments on standard graph benchmarks confirm that GTs scale comparably to GNNs, and a real‑world shortest‑path estimation task demonstrates their practical efficiency on large terrains.

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