Chapter

Structural, Syntactic, and Statistical Pattern Recognition

Volume 4109 of the series Lecture Notes in Computer Science pp 163-172

Fast Suboptimal Algorithms for the Computation of Graph Edit Distance

  • Michel NeuhausAffiliated withInstitute of Computer Science and Applied Mathematics, University of Bern
  • , Kaspar RiesenAffiliated withInstitute of Computer Science and Applied Mathematics, University of Bern
  • , Horst BunkeAffiliated withInstitute of Computer Science and Applied Mathematics, University of Bern

Abstract

Graph edit distance is one of the most flexible mechanisms for error-tolerant graph matching. Its key advantage is that edit distance is applicable to unconstrained attributed graphs and can be tailored to a wide variety of applications by means of specific edit cost functions. Its computational complexity, however, is exponential in the number of vertices, which means that edit distance is feasible for small graphs only. In this paper, we propose two simple, but effective modifications of a standard edit distance algorithm that allow us to suboptimally compute edit distance in a faster way. In experiments on real data, we demonstrate the resulting speedup and show that classification accuracy is mostly not affected. The suboptimality of our methods mainly results in larger inter-class distances, while intra-class distances remain low, which makes the proposed methods very well applicable to distance-based graph classification.