论文标题
端到端图卷积内核支持向量机
An End-to-End Graph Convolutional Kernel Support Vector Machine
论文作者
论文摘要
提出了用于图形分类的新型基于内核的支持向量机（SVM）。 SVM特征空间映射由一系列图形卷积层组成，该层为每个顶点生成矢量空间表示形式，然后是一个池层，该层为图形生成复制的内核Hilbert Space（RKHS）表示图。 RKHS的使用提供了使用内核函数在此空间中隐式操作的能力，而无需明确映射其中的计算复杂性。提出的模型以监督的端到端方式进行了训练，从而相对于正则分类损失，共同优化了卷积层，内核函数和SVM参数。这种方法与现有的基于内核的图形分类模型不同，该模型使用功能工程或无监督的学习来定义内核函数。实验结果表明，所提出的模型在许多数据集上的现有深度学习基线模型优于现有的深度学习基线模型。
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.
