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.

翻译：用于图嵌入的新算法降低了寻找低维表示的渐近复杂度。独热图编码器嵌入（GEE）通过对边进行单次线性扫描，生成渐近收敛于谱嵌入的表示。该方法的扩展性和性能优势受限于其在解释型语言中的串行实现。我们将GEE重构为Ligra图引擎中的并行程序，该程序在图的边上映射函数，并使用无锁原子指令防止数据竞争。在具有18亿条边的图上，该实现相比原始实现实现了500倍的加速，相比即时编译版本实现了17倍的加速。