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 mapping into it. The proposed model is trained in a supervised end-to-end manner whereby the convolutional layers, the kernel function and SVM parameters are jointly optimized with respect to a regularized classification loss. This approach is distinct from existing kernel-based graph classification models which instead either use feature engineering or unsupervised learning to define the kernel function. Experimental results demonstrate that the proposed model outperforms existing deep learning baseline models on a number of datasets.

中文翻译：

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端到端图卷积核支持向量机

提出了一种新颖的基于核的图矢量分类支持向量机。SVM特征空间映射由一系列图形卷积层组成，该序列为每个顶点生成一个矢量空间表示，然后是一个池化层，为该图形生成一个再生内核希尔伯特空间（RKHS）表示。RKHS的使用提供了使用内核函数在此空间中隐式操作的功能，而无需显式映射到该空间中的计算复杂性。以监督的端到端方式训练提出的模型，从而针对正则化分类损失共同优化卷积层，核函数和SVM参数。这种方法不同于现有的基于内核的图分类模型，后者使用特征工程或无监督学习来定义内核功能。实验结果表明，该模型在许多数据集上均优于现有的深度学习基线模型。