论文标题
邻里保存归因图的内核
Neighborhood Preserving Kernels for Attributed Graphs
论文作者
论文摘要
我们描述了适用于归因图的复制内核的设计，其中两个图之间的相似性是根据图形公式的图形节点的邻域信息来定义的。我们将提出的内核表示为另外两个内核的加权总和，其中一个是一个R-Convolution核心内核，该内核处理图形的属性信息，另一个是处理标签信息的最佳分配内核。它们的配方方式使得作为内核计算的一部分处理的边缘具有相同的邻域属性，因此内核提出的将在图中处理的区域之间定义明确的对应关系。这些概念也扩展到了最短路径的情况。我们确定了可以映射到这样的邻里保存框架的最先进的内核。我们发现，可以从我们的方法中配制的产品图递归获得参数图中参数图的内核值。通过在支持向量机上纳入所提出的内核，我们分析了现实世界数据集，与其他最先进的图形内核相比，它显示出了出色的性能。
We describe the design of a reproducing kernel suitable for attributed graphs, in which the similarity between the two graphs is defined based on the neighborhood information of the graph nodes with the aid of a product graph formulation. We represent the proposed kernel as the weighted sum of two other kernels of which one is an R-convolution kernel that processes the attribute information of the graph and the other is an optimal assignment kernel that processes label information. They are formulated in such a way that the edges processed as part of the kernel computation have the same neighborhood properties and hence the kernel proposed makes a well-defined correspondence between regions processed in graphs. These concepts are also extended to the case of the shortest paths. We identified the state-of-the-art kernels that can be mapped to such a neighborhood preserving framework. We found that the kernel value of the argument graphs in each iteration of the Weisfeiler-Lehman color refinement algorithm can be obtained recursively from the product graph formulated in our method. By incorporating the proposed kernel on support vector machines we analyzed the real-world data sets and it has shown superior performance in comparison with that of the other state-of-the-art graph kernels.
