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.
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Vinh, N.D., Inokuchi, A., Washio, T. (2010). Graph Classification Based on Optimizing Graph Spectra. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds) Discovery Science. DS 2010. Lecture Notes in Computer Science(), vol 6332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16184-1_15
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DOI: https://doi.org/10.1007/978-3-642-16184-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16183-4
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