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arXiv · 2402.04403

Edge-Parallel Graph Encoder Embedding

Ariel Lubonja·Cencheng Shen·Carey Priebe·Randal Burns
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Abstract

New algorithms for embedding graphs have reduced the asymptotic complexity of finding low-dimensional representations. One-Hot Graph Encoder Embedding (GEE) uses a single, linear pass over edges and produces an embedding that converges asymptotically to the spectral embedding. The scaling and performance benefits of this approach have been limited by a serial implementation in an interpreted language. We refactor GEE into a parallel program in the Ligra graph engine that maps functions over the edges of the graph and uses lock-free atomic instrutions to prevent data races. On a graph with 1.8B edges, this results in a 500 times speedup over the original implementation and a 17 times speedup over a just-in-time compiled version.

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Ariel Lubonja, Cencheng Shen, Carey Priebe, Randal Burns. 2024-02-06. Edge-Parallel Graph Encoder Embedding. https://arxiv.org/abs/2402.04403

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