Abstract
In network data analysis, visualization of network data plays an important role. The aim of graph embedding (or graph drawing) is to get the geometric representation of graphs by mapping vertices to points in some manifold including Euclidean space. There is a large literature on graph embedding. However, most graph embedding methods mainly focus on the visibility of the geometric representation. Currently, the graph embedding problem has drawn quite some attention in the machine learning community. There are some important results about how to reconstruct the original structure of the network in Euclidean space. In this paper, we introduce four important graph embedding methods that focus on the geometric structure of unweighted graphs.
