Leveraging matrix computations for efficient learning on graphs

While several kernel functions for graphs have been proposed in the past, their practical applications so far are rather limited. One reason for this could be the fact that graphs are typically large, but applying standard implementations of kernel methods with graph kernels is not the most efficient solution. In this paper, we describe several ways to implement efficient kernel methods for graphs using tools from matrix computations.