Graph Kernels: A Survey
Graph Kernels: A Survey
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DOI：
10.1613/jair.1.13225
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Giannis Nikolentzos;Giannis Siglidis;M. Vazirgiannis
中科院分区：
文献类型：
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作者：
Giannis Nikolentzos;Giannis Siglidis;M. Vazirgiannis
在过去的十年中，图核引起了人们的广泛关注，并已发展成为结构化数据学习的一个快速发展的分支。在过去的20年中，该领域的大量研究活动导致了数十种图核的发展，每种都专注于图的特定结构特性。图核已被证明在从社交网络到生物信息学的广泛领域中是成功的。本调查的目标是提供一个统一的视图的文献图内核。特别是，我们提出了广泛的图形内核的全面概述。此外，我们在公开的数据集上对其中几个内核进行了实验评估，并进行了比较研究。最后，我们讨论了图内核的关键应用，并概述了一些有待解决的挑战。
Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years, the considerable research activity that occurred in the field resulted in the development of dozens of graph kernels, each focusing on specific structural properties of graphs. Graph kernels have proven successful in a wide range of domains, ranging from social networks to bioinformatics. The goal of this survey is to provide a unifying view of the literature on graph kernels. In particular, we present a comprehensive overview of a wide range of graph kernels. Furthermore, we perform an experimental evaluation of several of those kernels on publicly available datasets, and provide a comparative study. Finally, we discuss key applications of graph kernels, and outline some challenges that remain to be addressed.