Abstract
Kernel methods such as the SVM are becoming increasingly popular due to their high performance in graph classification. In this paper, we propose a novel graph kernel, called SPEC, based on graph spectra and the Interlace Theorem, as well as an algorithm, called OPTSPEC, to optimize the SPEC kernel used in an SVM for graph classification. The fundamental performance of the method is evaluated using artificial datasets, and its practicality confirmed through experiments using a real-world dataset. © 2010 Springer-Verlag.
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Vinh, N. D., Inokuchi, A., & Washio, T. (2010). Graph classification based on optimizing graph spectra. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6332 LNAI, pp. 205–220). https://doi.org/10.1007/978-3-642-16184-1_15
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