LAUSR

Abstract In this paper, we propose a general framework that, starting from the feature space of an existing base graph kernel, allows to define more expressive kernels which can learn more complex concepts, meanwhile generalizing different proposals in literature. Experimental results on eight real-world graph datasets from different domains show that the proposed framework instances are able to get a statistically significant performance improvement over both the considered base kernels and framework instances previously defined in literature, obtaining state-of-the-art results on all the considered datasets.