Predictive Graph Mining with Kernel Methods

Abstract

Graphs are a major tool for modeling objects with complex data structures. Devising learning algorithms that are able to handle graph representations is thus a core issue in knowledge discovery with complex data. While a significant amount of recent research has been devoted to inducing functions on the vertices of the graph, we concentrate on the task of inducing a function on the set of graphs. Application areas of such learning algorithms range from computer vision to biology and beyond. Here, we present a number of results on extending kernel methods to complex data, in general, and graph representations, in particular. With the very good performance of kernel methods on data that can easily be embedded in a Euclidean space, kernel methods have the potential to overcome some of the major weaknesses of previous approaches to learning from complex data. In order to apply kernel methods to graph data, we propose two different kernel functions and compare them on a relational reinforcement learning problem and a molecule classification problem.