One-Hot Graph Encoder Embedding
One-Hot Graph Encoder Embedding
复制标题
One-Hot 图编码器嵌入
DOI：
10.1109/tpami.2022.3225073
复制
发表时间：
2023
影响因子：
23.6
通讯作者：
Priebe, Carey E.
中科院分区：
文献类型：
--
作者：
Shen, Cencheng;Wang, Qizhe;Priebe, Carey E.
在这篇文章中，我们提出了一种快速的图嵌入方法，称为one-hot graph encoder embedding。它具有线性计算复杂性，并且能够在标准PC上在几分钟内处理数十亿条边-使其成为大型图形处理的理想候选者。它既适用于邻接矩阵，也适用于图的拉普拉斯算子，可以看作是谱嵌入的一种变换。在随机图模型下，图编码器嵌入近似正态分布，并渐近收敛到其平均值。我们展示了三个应用程序：顶点分类，顶点聚类和图形引导。在每种情况下，图形编码器嵌入都表现出无与伦比的计算优势。
In this article 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.
影响因子：
1.1
作者：
Alden Green;C. Shalizi
通讯作者：
C. Shalizi
影响因子：
2.7
作者：
Lee, Youjin;Shen, Cencheng;Vogelstein, Joshua T.
通讯作者：
Vogelstein, Joshua T.