Graph Kernels

被引：48

作者：

Borgwardt, Karsten ^{[1
]}

Ghisu, Elisabetta ^{[1
]}

Llinares-Lopez, Felipe ^{[1
]}

O'Bray, Leslie ^{[1
]}

Rieck, Bastian ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, Machine Learning & Computat Biol Lab, D BSSE, Swiss Inst Bioinformat, Basel, Switzerland

来源：

FOUNDATIONS AND TRENDS IN MACHINE LEARNING | 2020年 / 13卷 / 5-6期

关键词：

CLASSIFICATION; ALGORITHMS; PREDICTION;

D O I：

10.1561/2200000076

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：531 / 712

页数：182

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