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Multiview graph kernel based on popular methods

Graph classification is a challenging problem that assigns the structured data into several categories. The success of kernel methods in graph classification has aroused many designs of novel graph kernel. In this paper, we present a multiview graph kernel, a new method to combine the advantages of multiple kernel functions (Graphlet kernel and Weisfeiler-Lehman kernel). Experiments on several benchmark datasets show that multiview graph kernel could achieve significant improvements compared with the original graph kernels.