Abstract |
The diffusion kernel is a general method for computing
pairwise distances among all nodes in a graph, based upon the sum of
weighted paths between each pair of nodes. This technique
has been used successfully, in conjunction with kernel-based learning
methods, to draw inferences from several types of biological networks. We
show that computing the diffusion kernel is equivalent to maximizing
the von Neumann entropy, subject to a global constraint on the sum
of the Euclidean distances between nodes. This global constraint
allows for high variance in the pairwise distances. Accordingly,
we propose an alternative, locally constrained diffusion kernel,
and we demonstrate that the resulting kernel allows for more accurate
support vector machine prediction of protein functional classifications
from metabolic and protein-protein interaction networks. |