Exploiting the ODD framework to define a novel effective graph kernel

In this paper, we show how the Ordered Decomposition DAGs kernel framework, a framework that allows the denition of graph kernels from tree kernels, allows to easily dene new state-of-the-art graph kernels. Here we consider a quite fast graph kernel based on the Subtree kernel (ST), and we improve it by increasing its expressivity by adding new features involving partial tree features. While the worst-case complexity of the new obtained graph kernel does not increase, its eectiveness is improved, as shown on several chemical datasets, reaching state-of-the-art performances.