GraKeL: A Graph Kernel Library in Python - Archive ouverte HAL Access content directly
Journal Articles Journal of Machine Learning Research Year : 2020

GraKeL: A Graph Kernel Library in Python

Abstract

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at: https://github.com/ysig/ GraKeL.
Fichier principal
Vignette du fichier
18-370.pdf (302.51 Ko) Télécharger le fichier
Origin : Publication funded by an institution
Loading...

Dates and versions

hal-02612740 , version 1 (19-05-2020)

Identifiers

Cite

Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, et al.. GraKeL: A Graph Kernel Library in Python. Journal of Machine Learning Research, 2020. ⟨hal-02612740⟩
161 View
101 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More