Message Passing Graph Kernels
Message Passing Graph Kernels
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消息传递图内核
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
M. Vazirgiannis
中科院分区：
文献类型：
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作者：
Giannis Nikolentzos;M. Vazirgiannis
图核方法是最近出现的一种同时处理图相似性和学习任务的很有前途的方法。在这篇文章中，我们提出了一个通用的图核设计框架。该框架利用了著名的基于图的消息传递方案。从框架派生的内核由两个组件组成。第一个分量是顶点之间的核，而第二个分量是图之间的核。提出的框架背后的主要思想是使用迭代过程隐式地更新顶点的表示。然后，这些表示充当比较图对的内核的构建块。我们推导了该框架的四个实例，并通过大量的实验表明，这些实例在各种任务中与最先进的方法具有竞争力。
Graph kernels have recently emerged as a promising approach for tackling the graph similarity and learning tasks at the same time. In this paper, we propose a general framework for designing graph kernels. The proposed framework capitalizes on the well-known message passing scheme on graphs. The kernels derived from the framework consist of two components. The first component is a kernel between vertices, while the second component is a kernel between graphs. The main idea behind the proposed framework is that the representations of the vertices are implicitly updated using an iterative procedure. Then, these representations serve as the building blocks of a kernel that compares pairs of graphs. We derive four instances of the proposed framework, and show through extensive experiments that these instances are competitive with state-of-the-art methods in various tasks.