Learning graph-matching substitution costs based on the optimality of the Oracle’s correspondence

Abstract

Throughout the last 30 years, several methods have been presented to perform error-tolerant graph matching. All of these methods assume there are some given weights that gauge the importance of each of attributes on nodes or edges. These weights are supposed to be manually validated and little research have been done to automatically learn the best combination of weights such that the resulting graph matching problem best matches the expected solution than an expert (human or artificial) would provide. We present an optimisation function (Loss function and Regularisation term) to automatically find these weights. Our practical evaluation reveals that our method properly learns these weights since applying the learned weights, the automatically obtained labelling between nodes is closer to the oracle’s labelling than applying the non-learned weights.