Graph embedding in vector spaces by node attribute statistics

  • Authors:

  • Jaume Gibert;Ernest Valveny;Horst Bunke

  • Affiliations:

  • Computer Vision Center, Universitat Autònoma de Barcelona, Edifici O, Campus UAB, 08193 Bellaterra, Spain;Computer Vision Center, Universitat Autònoma de Barcelona, Edifici O, Campus UAB, 08193 Bellaterra, Spain;Institute for Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland

  • Venue:
  • Pattern Recognition
  • Year:
  • 2012

Quantified Score

Hi-index 0.01

Visualization

Abstract

Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graph embedding into vector spaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous node attributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs.