Ordered Decompositional DAG Kernels Enhancements

TL;DR

This paper enhances the Ordered Decomposition DAG kernel framework by introducing a more expressive, weighted graph kernel based on the Subtree kernel, achieving state-of-the-art classification results on real-world datasets.

Contribution

The paper proposes new enhancements to the ODD kernel framework, including increased expressiveness and a novel feature weighting scheme, leading to improved graph classification performance.

Findings

Improved classification accuracy on multiple datasets.

Enhanced expressiveness without increasing worst-case complexity.

Achieved state-of-the-art results with the proposed kernels.

Abstract

In this paper, we show how the Ordered Decomposition DAGs (ODD) kernel framework, a framework that allows the definition of graph kernels from tree kernels, allows to easily define new state-of-the-art graph kernels. Here we consider a fast graph kernel based on the Subtree kernel (ST), and we propose various enhancements to increase its expressiveness. The proposed DAG kernel has the same worst-case complexity as the one based on ST, but an improved expressivity due to an augmented set of features. Moreover, we propose a novel weighting scheme for the features, which can be applied to other kernels of the ODD framework. These improvements allow the proposed kernels to improve on the classification performances of the ST-based kernel for several real-world datasets, reaching state-of-the-art performances.