Link enrichment for diffusion-based graph node kernels

Abstract

The notion of node similarity is key in many graph processing techniques and it is especially important in diffusion graph kernels. However, when the graph structure is affected by noise in the form of missing links, similarities are distorted proportionally to the sparsity of the graph and to the fraction of missing links. Here, we introduce the notion of link enrichment, that is, performing link prediction in order to improve the performance of diffusion-based kernels. We empirically show a robust and large effect for the combination of a number of link prediction and a number of diffusion kernel techniques on several gene-disease association problems.