GraKeL

by ysig

ysig / GraKeL

A scikit-learn compatible library for graph kernels

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Documentation | Paper

GraKeL is a library that provides implementations of several well-established graph kernels. The library unifies these kernels into a common framework. Furthermore, it provides implementations of some frameworks that work on top of graph kernels. Specifically, GraKeL contains 15 kernels and 2 frameworks. The library is compatible with the scikit-learn pipeline allowing easy and fast integration inside machine learning algorithms.


In detail, the following kernels and frameworks are currently implemented:


To learn how to install and use GraKeL, and to find out more about the implemented kernels and frameworks, please read our documentation. To learn about the functionality of the library and about example applications, check out our examples in the

examples/
directory and our tutorials in the
tutorials/
directory.

In case you find a bug, please open an issue. To propose a new kernel, you can open a feature request.

Installation

The GraKeL library requires the following packages to be installed:

  • Python (>=2.7, >=3.5)
  • NumPy (>=1.8.2)
  • SciPy (>=0.13.3)
  • Cython (>=0.27.3)
  • cvxopt (>=1.2.0) [optional]
  • future (>=0.16.0) (for python 2.7)

To install the package, run:

$ pip install grakel

Running tests

To test the package, execute:

sh
$ nosetests grakel

Running examples

$ cd examples
$ python shortest_path.py

Cite

If you use GraKeL in a scientific publication, please cite our paper (http://jmlr.org/papers/volume21/18-370/18-370.pdf):

@article{JMLR:v21:18-370,
  author  = {Giannis Siglidis and Giannis Nikolentzos and Stratis Limnios and Christos Giatsidis and Konstantinos Skianis and Michalis Vazirgiannis},
  title   = {GraKeL: A Graph Kernel Library in Python},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {54},
  pages   = {1-5}
}

License

GraKeL is distributed under the BSD 3-clause license. The library makes use of the C++ source code of BLISS (a tool for computing automorphism groups and canonical labelings of graphs) which is LGPL licensed. Futhermore, the cvxopt package (a software package for convex optimization) which is an optional dependency of GraKeL is GPL licensed.

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