Rotary Position Encodings for Graphs

Abstract:We study the extent to which rotary position encodings (RoPE), a recent transformer position encoding algorithm broadly adopted in large language models (LLMs) and vision transformers (ViTs), can be applied to graph-structured data. We find that rotating tokens depending on the spectrum of the graph Laplacian efficiently injects structural information into the attention mechanism, boosting performance in synthetic and real-world graph learning tasks. This approach, coined _Wave-Induced Rotary Encodings_ (WIRE), enjoys intriguing theoretical properties: it recovers regular RoPE on grids, and depends asymptotically on the graph effective resistance. Unlike bias-based relative position encodings, WIRE is compatible with linear attention.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2509.22259 [cs.LG]

(or arXiv:2509.22259v5 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2509.22259 Focus to learn more arXiv-issued DOI via DataCite

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