Dissimilarity space embedding of labeled graphs by a clustering-based compression procedure

We propose two variants of a general-purpose graph classification system which rely on a theoretical result that we prove in this paper. The result allows us to solve analytically the setting of a sequential clustering algorithm that is used for compressing the input labeled graphs represented in the dissimilarity space. As a consequence, we achieve a considerable asymptotic and practical speed-up of the overall classification system, maintaining state-of-the-art results in terms of test set classification accuracy on well-known benchmarking datasets of labeled graphs. The obtained speed-up makes the system one step closer towards the applicability to bigger labeled graphs and larger datasets.