A hausdorff heuristic for efficient computation of graph edit distance

Abstract

Graph edit distance is a flexible and powerful measure of dissimilarity between two arbitrarily labeled graphs. Yet its application is limited by the exponential time complexity involved when matching unconstrained graphs. We have recently proposed a quadratic-time approximation of graph edit distance based on Hausdorff matching, which underestimates the true distance. In order to implement verification systems for the approximation algorithm, efficiency improvements are needed for the computation of the true distance. In this paper, we propose a Hausdorff heuristic that employs the approximation algorithm itself as a heuristic function for efficient A computation of the graph edit distance. In an experimental evaluation on several data sets of the IAM graph database, substantial search space reductions and runtime speedups of one order of magnitude are reported when compared with plain A search. © 2014 Springer-Verlag Berlin Heidelberg.