Graph Kernels
Matthias Rupp
In: Machine Learning Approach for Network Analysis: Novel Graph Classes for Classification Techniques (2011) Wiley .

Abstract

Graph kernels are formal similarity measures defined directly on graphs. Because they are positive semi-definite functions, they correspond to inner products. This property makes them suitable for use with kernel-based machine learning algorithms, such as support vector machines and Gaussian processes. In this chapter, I present different types of graph kernels (based on random walks, shortest paths, tree patterns, cyclic patterns, graphlets, and optimal assignments), give an overview of successful applications in bio- and cheminformatics, and discuss advantages and limitations of kernels between graphs.

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