广义路袋形式图的协方差核和相关核
Covariance and Correlation Kernels on a Graph in the Generalized Bag-of-Paths Formalism
摘要：
这项工作推导出封闭形式的表达式计算期望的共存和同现的节点从网络中采样的路径，根据一般的路径权重（一袋路径）的数量。其基本思想是，当两个节点经常出现在网络的（最好是短的）路径上时，它们被认为是相似的。不同的表达式得到的规则和命中路径，并作为计算新的协方差和节点之间的相关性措施，这是有效的半正定核图的基础。半监督分类问题的实验表明，引入的相似性措施提供竞争力的结果相比，其他国家的最先进的节点之间的距离和相似性措施。
英文摘要：
This work derives closed-form expressions computing the expectation of co-presence and of number of co-occurrences of nodes on paths sampled from a network according to general path weights (a bag of paths). The underlying idea is that two nodes are considered as similar when they often appear together on (preferably short) paths of the network. The different expressions are obtained for both regular and hitting paths and serve as a basis for computing new covariance and correlation measures between nodes, which are valid positive semi-definite kernels on a graph. Experiments on semi-supervised classification problems show that the introduced similarity measures provide competitive results compared to other state-of-the-art distance and similarity measures between nodes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
