Graph Kernels: State-of-the-Art and Future Challenges

Autor:	Karsten M. Borgwardt, Felipe Llinares-López, Elisabetta Ghisu, Bastian Rieck, Leslie O'Bray
Rok vydání:	2020
Předmět:	FOS: Computer and information sciences Human-Computer Interaction Computer Science - Machine Learning Theoretical computer science Statistics - Machine Learning Artificial Intelligence Computer science Kernel (statistics) Machine Learning (stat.ML) Software Graph Machine Learning (cs.LG)
Zdroj:	Foundations and Trends® in Machine Learning
ISSN:	1935-8245 1935-8237
DOI:	10.1561/2200000076
Popis:	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. Accepted by Foundations and Trends in Machine Learning, 2020
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::512d61a8912a421a446186bae3302e45 https://doi.org/10.1561/2200000076 Zobrazit plný text záznamu