A Fast Jensen-Shannon Subgraph Kernel

Lu Bai, Edwin R. Hancock

Abstract: In this paper we present a fast subgraph kernel based on Jensen-Shannon divergence and depth-based representations. For graphs with n vertices and m edges, the worst-case time complexity for our kernel is O(n 3 + mn), in contrast to O(n 6) for the classic graph kernel. Key to this efficiency is that we manage to compute the Jensen-Shannon divergence involved in our kernel with O(n 2) operations. This computational strategy enables our subgraph kernel to easily scale up to graphs of reasonably large sizes and thus overcome the size limits arising in state of the art graph kernels. Experiments on standard bioinformatics graph datasets together with graph datasets extracted from images demonstrate the effectiveness and efficiency of our subgraph kernel.