论文标题
非属性图的分层及其透射对齐的图表
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
论文作者
论文摘要
在本文中，我们通过通过层次原型图的家族将图形在图形之间对准图形，从而开发了一个新的图内核，即分层横向分配的内核。与大多数现有的最新图形内核相比，拟议的内核具有三个理论优势。首先，它将图之间的位置对应信息纳入内核计算中，从而克服了在大多数R-Convolution内核中忽略的结构对应关系的缺点。其次，它保证了大多数现有匹配内核无法使用的通信信息之间的传递性。第三，它将比较中所有图的信息都包含到内核计算过程中，从而封装了更丰富的特征。通过转导训练C-SVM分类器，实验评估证明了新的透射式一致核的有效性。就分类精度而言，所提出的内核可以优于基于标准图的数据集上的最新图形内核。
In this paper, we develop a new graph kernel, namely the Hierarchical Transitive-Aligned kernel, by transitively aligning the vertices between graphs through a family of hierarchical prototype graphs. Comparing to most existing state-of-the-art graph kernels, the proposed kernel has three theoretical advantages. First, it incorporates the locational correspondence information between graphs into the kernel computation, and thus overcomes the shortcoming of ignoring structural correspondences arising in most R-convolution kernels. Second, it guarantees the transitivity between the correspondence information that is not available for most existing matching kernels. Third, it incorporates the information of all graphs under comparisons into the kernel computation process, and thus encapsulates richer characteristics. By transductively training the C-SVM classifier, experimental evaluations demonstrate the effectiveness of the new transitive-aligned kernel. The proposed kernel can outperform state-of-the-art graph kernels on standard graph-based datasets in terms of the classification accuracy.
