Implicit and explicit graph embedding: Comparison of both approaches on chemoinformatics applications

Abstract

Defining similarities or distances between graphs is one of the bases of the structural pattern recognition field. An important trend within this field consists in going beyond the simple formulation of similarity measures by studying properties of graph's spaces induced by such distance or similarity measures. Such a problematic is closely related to the graph embedding problem. In this article, we investigate two types of similarity measures. The first one is based on the notion of graph edit distance which aims to catch a global dissimilarity between graphs. The second family is based on comparisons of bags of patterns extracted from graphs to be compared. Both approaches are detailed and their performances are evaluated on different chemoinformatics problems. © 2012 Springer-Verlag Berlin Heidelberg.