Local clique merging: An extension of the maximum common subgraph measure with applications in structural bioinformatics

Abstract

We develop a novel similarity measure for node-labeled and edgeweighted graphs, which is an extension of the well-known maximum common subgraph (MCS) measure. Despite its common usage and appealing properties, the MCS also exhibits some disadvantages, notably a lack of flexibility and tolerance toward structural variation. In order to address these issues, we propose a generalization which is based on so-called quasi-cliques. A quasi-clique is a relaxation of a clique in the sense of being an "almost" complete subgraph. Thus, it increases flexibility and robustness toward structural variation. To construct a quasiclique, we make use of a heuristic approach, in which so-called local cliques are determined first and combined into larger (quasi-)cliques afterward.We also present applications of our novel similarity measure to the retrieval and classification of protein binding sites. © Springer International Publishing Switzerland 2013.