An end-to-end graph convolutional kernel support vector machine
Abstract 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 reproducin...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-07-01
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| Series: | Applied Network Science |
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| Online Access: | http://link.springer.com/article/10.1007/s41109-020-00282-2 |