Local entropic graphs for globally-consistent graph matching

Abstract

In this paper we propose a novel approach to obtain unambiguous and robust node attributes for matching non-attributed graphs. Such approach consists of exploiting the information coming from diffusion kernels to embed the subgraph induced by the neighborhood of each vertex in an Euclidean manifold and then use entropic graphs for measuring the α-entropy of the resulting distribution. Our experiments with random-generated graphs with 50 nodes show that at low edge densities, where the effect of structural noise is higher, this approach outperforms the description of the subgraph only in terms of diffusion kernels. Furthermore, our structural recognition experiments show that the approach has a practical application. All experiments were performed by weighting the well-known quadratic cost function used in the Softassign algorithm. © Springer-Verlag Berlin Heidelberg 2005.