论文标题
图表：最新和未来的挑战
Graph Kernels: State-of-the-Art and Future Challenges
论文作者
论文摘要
图形结构数据是许多应用领域的组成部分，包括化学信息学，计算生物学，神经影像学和社交网络分析。在过去的二十年中，已经提出了许多图内的图表，即图表之间的内核函数，以解决评估图之间的相似性的问题，从而可以在分类和回归设置中执行预测。该手稿对现有图表内核，其应用程序，软件以及数据资源以及最新图形内核的经验比较进行了审查。
Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis. Over the last two decades, numerous graph kernels, i.e. kernel functions between graphs, have been proposed to solve the problem of assessing the similarity between graphs, thereby making it possible to perform predictions in both classification and regression settings. This manuscript provides a review of existing graph kernels, their applications, software plus data resources, and an empirical comparison of state-of-the-art graph kernels.
