The graph edit distance measures the dissimilarity between two graphs based on minimum cost sequences of edit operations that transform one graph into the other. Typically, the user is supposed to define the costs of individual edit operations. This project aims to develop techniques for learning edit costs based on ground-truth graph distances.

Requirements: Interest in machine learning and optimization.

Contact: Nils Kriege