A tree-based kernel for graphs with continuous attributes

TL;DR

This paper introduces a novel tree-based graph kernel capable of handling continuous node attributes efficiently, outperforming existing methods in accuracy and speed on real-world datasets.

Contribution

It presents a new graph kernel for continuous attributes using tree structures, with an efficient approximation that maintains performance while reducing computational complexity.

Findings

The kernel outperforms existing methods on most datasets.

The approximated kernel achieves similar accuracy with faster computation.

Experimental results validate the effectiveness of the proposed approach.

Abstract

The availability of graph data with node attributes that can be either discrete or real-valued is constantly increasing. While existing kernel methods are effective techniques for dealing with graphs having discrete node labels, their adaptation to non-discrete or continuous node attributes has been limited, mainly for computational issues. Recently, a few kernels especially tailored for this domain, and that trade predictive performance for computational efficiency, have been proposed. In this paper, we propose a graph kernel for complex and continuous nodes' attributes, whose features are tree structures extracted from specific graph visits. The kernel manages to keep the same complexity of state-of-the-art kernels while implicitly using a larger feature space. We further present an approximated variant of the kernel which reduces its complexity significantly. Experimental results obtained on six real-world datasets show that the kernel is the best performing one on most of them. Moreover, in most cases the approximated version reaches comparable performances to current state-of-the-art kernels in terms of classification accuracy while greatly shortening the running times.