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https://github.com/ysig/GraKeL

A scikit-learn compatible library for graph kernels
https://github.com/ysig/GraKeL

bioinformatics chemoinformatics graph-classification graph-kernels graph-mining graph-similarity graph-similarity-algorithms scikit-learn

Last synced: about 2 months ago
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A scikit-learn compatible library for graph kernels

Host: GitHub
URL: https://github.com/ysig/GraKeL
Owner: ysig
License: other
Created: 2017-10-31T13:05:27.000Z (almost 7 years ago)
Default Branch: master
Last Pushed: 2024-04-23T14:54:43.000Z (5 months ago)
Last Synced: 2024-07-29T07:32:15.759Z (about 2 months ago)
Topics: bioinformatics, chemoinformatics, graph-classification, graph-kernels, graph-mining, graph-similarity, graph-similarity-algorithms, scikit-learn
Language: Python
Homepage: https://ysig.github.io/GraKeL/
Size: 26.5 MB
Stars: 587
Watchers: 10
Forks: 96
Open Issues: 8
Metadata Files:
- Readme: README.md
- License: LICENSE

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[![Pypi Versions](https://img.shields.io/pypi/pyversions/grakel.svg)](https://pypi.org/pypi/grakel/)

[![Coverage Status](https://codecov.io/gh/ysig/GraKeL/branch/master/graph/badge.svg)](https://codecov.io/gh/ysig/GraKeL)

[![CircleCI Status](https://circleci.com/gh/ysig/GraKeL.svg?style=svg)](https://circleci.com/gh/ysig/GraKeL)

**[Documentation](https://ysig.github.io/GraKeL/)** | **[Paper](http://jmlr.org/papers/volume21/18-370/18-370.pdf)**

*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 16 kernels and 2 frameworks. The library is compatible with the [scikit-learn](http://scikit-learn.org/) pipeline allowing easy and fast integration inside machine learning algorithms.

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

* **[Vertex histogram kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.VertexHistogram.html)**

* **[Edge histogram kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.EdgeHistogram.html)**

* **[Shortest path kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.ShortestPath.html)** from Borgwardt and Kriegel: [Shortest-path kernels on graphs](https://www.dbs.ifi.lmu.de/~borgward/papers/BorKri05.pdf) (ICDM 2005)

* **[Graphlet kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.GraphletSampling.html)** from Shervashidze *et al.*: [Efficient graphlet kernels for large graph comparison](http://proceedings.mlr.press/v5/shervashidze09a/shervashidze09a.pdf) (AISTATS 2009)

* **[Random walk kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.RandomWalk.html)** from Vishwanathan *et al.*: [Graph Kernels](http://www.jmlr.org/papers/volume11/vishwanathan10a/vishwanathan10a.pdf) (JMLR 11(Apr))

* **[Neighborhood hash graph kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.NeighborhoodHash.html)** from Hido and Kashima: [A Linear-time Graph Kernel](https://ieeexplore.ieee.org/abstract/document/5360243) (ICDM 2009)

* **[Weisfeiler-Lehman framework](https://ysig.github.io/GraKeL/latest/generated/grakel.WeisfeilerLehman.html)** from Shervashidze *et al.*: [Weisfeiler-Lehman Graph Kernels](http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf) (JMLR 12(Sep))

* **[Neighborhood subgraph pairwise distance kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.NeighborhoodSubgraphPairwiseDistance.html)** from Costa and De Grave: [Fast Neighborhood Subgraph Pairwise Distance Kernel](https://pdfs.semanticscholar.org/7a10/f6a406b664d1159e7c4fefbdd6ac275aee53.pdf) (ICML 2010)

* **[Lovasz-theta kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.LovaszTheta.html)** from Johansson *et al.*: [Global graph kernels using geometric embeddings](http://proceedings.mlr.press/v32/johansson14.pdf) (ICML 2014)

* **[SVM-theta kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.SvmTheta.html)** from Johansson *et al.*: [Global graph kernels using geometric embeddings](http://proceedings.mlr.press/v32/johansson14.pdf) (ICML 2014)

* **[Ordered decompositional DAG kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.OddSth.html)** from Da San Martino *et al.*: [A Tree-Based Kernel for Graphs](https://pdfs.semanticscholar.org/69ee/18dd7a214d4d656b5b95742212f050dabeac.pdf) (SDM 2012)

* **[GraphHopper kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.GraphHopper.html)** from Feragen *et al.*: [Scalable kernels for graphs with continuous attributes](https://papers.nips.cc/paper/5155-scalable-kernels-for-graphs-with-continuous-attributes.pdf) (NIPS 2013)

* **[Propagation kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.Propagation.html)** from Neumann *et al.*: [Propagation kernels: efficient graph kernels from propagated information](https://link.springer.com/content/pdf/10.1007/s10994-015-5517-9.pdf) (Machine Learning 102(2))

* **[Pyramid match kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.PyramidMatch.html)** from Nikolentzos *et al.*: [Matching Node Embeddings for Graph Similarity](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14494/14426) (AAAI 2017)

* **[Subgraph matching kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.SubgraphMatching.html)** from Kriege and Mutzel: [Subgraph Matching Kernels for Attributed Graphs](https://arxiv.org/ftp/arxiv/papers/1206/1206.6483.pdf) (ICML 2012)

* **[Multiscale Laplacian kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.MultiscaleLaplacian.html)** from Kondor and Pan: [The Multiscale Laplacian Graph Kernel](https://papers.nips.cc/paper/6135-the-multiscale-laplacian-graph-kernel.pdf) (NIPS 2016)

* **[Core framework](https://ysig.github.io/GraKeL/latest/generated/grakel.CoreFramework.html)** from Nikolentzos *et al.*: [A Degeneracy Framework for Graph Similarity](https://www.ijcai.org/proceedings/2018/0360.pdf) (IJCAI 2018)

* **[Weisfeiler-Lehman optimal assignment kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.WeisfeilerLehmanOptimalAssignment.html)** from Kriege *et al.*: [On Valid Optimal Assignment Kernels and Applications to Graph Classification](http://papers.nips.cc/paper/6166-on-valid-optimal-assignment-kernels-and-applications-to-graph-classification.pdf) (NIPS 2016)

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To learn how to install and use GraKeL, and to find out more about the implemented kernels and frameworks, please read our [documentation](https://ysig.github.io/GraKeL/). To learn about the functionality of the library and about example applications, check out our [examples](https://github.com/ysig/GraKeL/tree/master/examples) in the `examples/` directory and our [tutorials](https://github.com/ysig/GraKeL/tree/master/tutorials) in the `tutorials/` directory.

In case you find a bug, please open an [issue](https://github.com/ysig/GraKeL/issues). To propose a new kernel, you can open a [feature request](https://github.com/ysig/GraKeL/issues).

## 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:

```sh

$ pip install grakel

```

## Running tests

To test the package, execute:

```sh

$ pytest

```

## 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):

```bibtex

@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](http://www.tcs.hut.fi/Software/bliss) (a tool for computing automorphism groups and canonical labelings of graphs) which is __LGPL__ licensed. Futhermore, the [cvxopt](https://cvxopt.org/) package (a software package for convex optimization) which is an optional dependency of GraKeL is __GPL__ licensed.

## Acknowledgements

We would like to thank [@eddiebergman](https://github.com/eddiebergman) for modernizing our CI and extending our python support.