Density of States Graph Kernels
This work improves graph kernels for applications in bioinformatics, social networks, and computer vision, though it is incremental as it builds on existing spectral analysis frameworks.
The paper tackled the problem of quantifying similarity between graphs by addressing the slowness and tottering issues of random walk kernels, resulting in higher classification accuracies on benchmark datasets.
A fundamental problem on graph-structured data is that of quantifying similarity between graphs. Graph kernels are an established technique for such tasks; in particular, those based on random walks and return probabilities have proven to be effective in wide-ranging applications, from bioinformatics to social networks to computer vision. However, random walk kernels generally suffer from slowness and tottering, an effect which causes walks to overemphasize local graph topology, undercutting the importance of global structure. To correct for these issues, we recast return probability graph kernels under the more general framework of density of states -- a framework which uses the lens of spectral analysis to uncover graph motifs and properties hidden within the interior of the spectrum -- and use our interpretation to construct scalable, composite density of states based graph kernels which balance local and global information, leading to higher classification accuracies on a host of benchmark datasets.