Edit Distance Based Kernel Functions for Attributed Graph Matching

In this paper we propose the use of a simple kernel function based on the graph edit distance. The kernel function allows us to apply a wide range of statistical algorithms to the problem of attributed graph matching. The function we describe is simple to compute and leads to several convenient interpretations of geometric properties of graphs in their implicit vector space representation. Although the function is not generally positive definite, we show in experiments on real-world data that the kernel approach may result in a significant improvement of the graph matching and classification performance using support vector machines and kernel principal component analysis.

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