Machine learning on graphs with kernels

Graphs are becoming a dominant structure in current information management with many domains involved, including social networks, chemistry, biology, etc. Many real-world problems require applying machine learning tasks to graph-structured data. Graph kernels have emerged as a promising approach for dealing with these tasks. A graph kernel is a symmetric, positive semidefinite function on the set of graphs. These functions extend the applicability of kernel methods to graphs. Graph kernels have attracted a lot of attention during the last 20 years. The considerable research activity that occurred in the field resulted in the development of dozens of kernels, each focusing on specific structural properties of graphs. The goal of this tutorial is to offer a comprehensive presentation of a wide range of graph kernels, and to describe their key applications. The tutorial will also offer to the participants hands-on experience in applying graph kernels to classification problems.