Approximation Of Graph Edit Distance By Means Of A Utility Matrix

Graph edit distance is one of the most popular graph matching paradigms available. By means of a reformulation of graph edit distance to an instance of a linear sum assignment problem, the major drawback of this dissimilarity model, viz. the exponential time complexity, has been invalidated recently. Yet, the substantial decrease of the computation time is at the expense of an approximation error. The present paper introduces a novel transformation that processes the underlying cost model into a utility model. The benefit of this transformation is that it enables the integration of additional information in the assignment process. We empirically confirm the positive effects of this transformation on three standard graph data sets. That is, we show that the accuracy of a distance based classifier can be improved with the proposed transformation while the run time remains nearly unaffected.