DEEP: decomposition feature enhancement procedure for graphs

When dealing with machine learning on graphs, one of the most successfully approaches is the one of kernel methods. Depending if one is interested in predicting properties of graphs (e.g. graph classification) or to predict properties of nodes in a single graph (e.g. graph node classification), different kernel functions should be adopted. In the last few years, several kernels for graphs have been defined in literature that extract local features from the input graphs, obtaining both efficiency and state-of-the-art predictive performances. Recently, some work has been done in this direction also regarding graph node kernels, but the majority of the graph node kernels available in literature consider only global information, that can be not optimal for many tasks. In this paper, we propose a procedure that allows to transform a local graph kernel in a kernel for nodes in a single, huge graph. We apply a specific instantiation to the task of disease gene prioritization from the bioinformatics domain, improving the state of the art in many diseases.