Jensen-Shannon graph kernel using information functionals

In recent work we have shown how to use the von Neumann entropy to construct a Jensen-Shannon kernel on graphs. The kernel is defined as the difference in entropies between a product graph and the separate graphs being compared. To develop this graph kernel further, in this paper we explore how to render the computation of the Jensen-Shannon kernel more efficient by using the information functionals defined by Dehmer to compute the required entropies. We illustrate how the resulting Jensen-Shannon graph kernels can be used for the purposes of graph clustering. Experimental results reveal that the methods gives good classification performance on graphs extracted from an object recognition dataset and several bioinformatics datasets.