A Comparison of Graph Embedding Methods for Vertex Nomination

Given an attributed graph representation of data, vertex nomination works to find the group of vertices which are of interest, e.g., those vertices whose attributes are different from others´, or the connection among those vertices are more frequent. In this paper we present an algorithm to estimate the power of nominating these interesting vertices. This algorithm is based on Wilcoxon rank sum test. It requires to embed graph vertices into a low dimensional space. Two graph embedding methods, adjacency spectral embedding and multidimensional scaling composed with canonical correlation analysis are employed. We investigate a case where two graphs are available for modeling the same objects in different spaces, and show the effects of data fusion on vertex nomination power.