# An end-to-end graph convolutional kernel support vector machine

@article{Corcoran2020AnEG, title={An end-to-end graph convolutional kernel support vector machine}, author={Padraig Corcoran}, journal={Applied Network Science}, year={2020}, volume={5}, pages={1-15} }

A novel kernel-based support vector machine (SVM) for graph classification is proposed. The SVM feature space mapping consists of a sequence of graph convolutional layers, which generates a vector space representation for each vertex, followed by a pooling layer which generates a reproducing kernel Hilbert space (RKHS) representation for the graph. The use of a RKHS offers the ability to implicitly operate in this space using a kernel function without the computational complexity of explicitly…

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