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

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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Bibliographic Details
Main Author: Padraig Corcoran
Format: Article
Language:English
Published: SpringerOpen 2020-07-01
Series:Applied Network Science
Subjects:
Graph neural network
Kernel method
Support vector machine
Online Access:http://link.springer.com/article/10.1007/s41109-020-00282-2

Internet

http://link.springer.com/article/10.1007/s41109-020-00282-2

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