In this paper we propose a lightning fast graph embedding method called one-hot graph encoder embedding. It has a linear computational complexity and the capacity to process billions of edges within minutes on standard PC -- making it an ideal candidate for huge graph processing. It is applicable to either adjacency matrix or graph Laplacian, and can be viewed as a transformation of the spectral embedding. Under random graph models, the graph encoder embedding is approximately normally distributed per vertex, and asymptotically converges to its mean. We showcase three applications: vertex classification, vertex clustering, and graph bootstrap. In every case, the graph encoder embedding exhibits unrivalled computational advantages.

翻译：在本文中,我们提出了一个称为单热图形编码器嵌入的闪电快速图形嵌入方法。它具有线性计算复杂性和在标准 PC 的分钟内处理数十亿边缘的能力,使它成为巨型图形处理的理想对象。它适用于相邻矩阵或图 Laplacian, 并且可以被视为光谱嵌入的转换。 在随机图形模型中, 图形编码器嵌入的通常分布于每个脊椎, 并且不同时会与其平均值相交。 我们展示了三种应用: 脊椎分类、 脊椎组合和图形靴子捕捉。 在每一种情况下, 图形编码器嵌入的图样都具有无增值的计算优势。