论文标题
使用FastMap-D将有向图嵌入潜在字段中
Embedding Directed Graphs in Potential Fields Using FastMap-D
论文作者
论文摘要
将无向图嵌入欧几里得空间中具有许多计算益处。 FastMap是一种有效的嵌入算法，可促进对无向图上提出的问题的几何解释。但是，欧几里得距离本质上是对称的，因此，欧几里得嵌入不能用于有向图。在本文中，我们提出了FastMap-D，这是对有向图的快速图的有效概括。 FastMap-D使用电势场嵌入顶点，以捕获有向图中成对距离之间的不对称性。 FastMap-D学习了使用机器学习模块来定义潜在字段的潜在功能。在对各种有向图的实验中，我们证明了FastMap-D的优势，而不是其他方法。
Embedding undirected graphs in a Euclidean space has many computational benefits. FastMap is an efficient embedding algorithm that facilitates a geometric interpretation of problems posed on undirected graphs. However, Euclidean distances are inherently symmetric and, thus, Euclidean embeddings cannot be used for directed graphs. In this paper, we present FastMap-D, an efficient generalization of FastMap to directed graphs. FastMap-D embeds vertices using a potential field to capture the asymmetry between the pairwise distances in directed graphs. FastMap-D learns a potential function to define the potential field using a machine learning module. In experiments on various kinds of directed graphs, we demonstrate the advantage of FastMap-D over other approaches.
