A tree-based kernel for graphs with continuous attributes
This addresses a computational bottleneck for researchers and practitioners working with graph data that has continuous attributes, offering an incremental improvement over existing methods.
The paper tackles the problem of graph kernels for graphs with continuous node attributes, which are limited by computational issues, by proposing a tree-based kernel that maintains state-of-the-art complexity while implicitly using a larger feature space, with experimental results showing it is the best performing on most of six real-world datasets and an approximated version achieves comparable accuracy with reduced running times.
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.