1 Kernels for Graphs
This chapter discusses the construction of kernel functions between labeled graphs. We provide a unified account of a family of kernels called label sequence kernels that are defined via label sequences generated by graph traversal. For cyclic graphs, dynamic programming techniques cannot simply be applied, because the kernel is based on an infinite dimensional feature space. We show that the kernel computation boils down to obtaining the stationary state of a discrete-time linear system, which is efficiently performed by solving simultaneous linear equations. Promising empirical results are presented in classification of chemical compounds.