Prototype selection for graph embedding using instance selection

Abstract

Currently, graph embedding has taken a great interest in the area of structural pattern recognition, especially techniques based on representation via dissimilarity. However, one of the main problems of this technique is the selection of a suitable set of prototype graphs that better describes the whole set of graphs. In this paper, we evaluate the use of an instance selection method based on clustering for graph embedding, which selects border prototypes and some non-border prototypes. An experimental evaluation shows that the selected method gets competitive accuracy and better runtimes than other state of the art methods.