| Name | GraKeL JSON |
| Version |
0.1.10
JSON |
| download |
| home_page | |
| Summary | A scikit-learn compatible library for graph kernels |
| upload_time | 2023-10-27 10:15:14 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.5 |
| license | New BSD License Copyright (c) 2018- The grakel developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: a. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. b. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. c. Neither the name of the Scikit-learn Developers nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
cython
scikit-learn
six
future
joblib
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<p align="center">
<img width="50%" src="https://raw.githubusercontent.com/ysig/GraKeL/0.1a7/doc/_figures/logo.svg?sanitize=true" />
</p>
--------------------------------------------------------------------------------
[](https://pypi.org/pypi/grakel/)
[](https://codecov.io/gh/ysig/GraKeL)
[](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.
--------------------------------------------------------------------------------
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)
--------------------------------------------------------------------------------
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.
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"description": "<p align=\"center\">\n <img width=\"50%\" src=\"https://raw.githubusercontent.com/ysig/GraKeL/0.1a7/doc/_figures/logo.svg?sanitize=true\" />\n</p>\n\n--------------------------------------------------------------------------------\n\n[](https://pypi.org/pypi/grakel/)\n[](https://codecov.io/gh/ysig/GraKeL)\n[](https://circleci.com/gh/ysig/GraKeL)\n\n**[Documentation](https://ysig.github.io/GraKeL/)** | **[Paper](http://jmlr.org/papers/volume21/18-370/18-370.pdf)**\n\n*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.\n\n--------------------------------------------------------------------------------\n\nIn detail, the following kernels and frameworks are currently implemented:\n\n* **[Vertex histogram kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.VertexHistogram.html)**\n* **[Edge histogram kernel](https://ysig.github.io/GraKeL/latest/generated/grakel.EdgeHistogram.html)**\n* **[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)\n* **[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)\n* **[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))\n* **[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)\n* **[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))\n* **[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)\n* **[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)\n* **[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)\n* **[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)\n* **[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)\n* **[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))\n* **[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)\n* **[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)\n* **[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)\n* **[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)\n* **[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)\n\n--------------------------------------------------------------------------------\n\nTo 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.\n\nIn 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).\n\n## Installation\n\nThe GraKeL library requires the following packages to be installed:\n\n* Python (>=2.7, >=3.5)\n* NumPy (>=1.8.2)\n* SciPy (>=0.13.3)\n* Cython (>=0.27.3)\n* cvxopt (>=1.2.0) [optional]\n* future (>=0.16.0) (for python 2.7)\n\nTo install the package, run:\n\n```sh\n$ pip install grakel\n```\n\n## Running tests\n\nTo test the package, execute:\n```sh\n$ pytest\n```\n\n## Running examples\n\n```\n$ cd examples\n$ python shortest_path.py\n```\n\n## Cite\n\nIf you use GraKeL in a scientific publication, please cite our paper (http://jmlr.org/papers/volume21/18-370/18-370.pdf):\n\n```bibtex\n@article{JMLR:v21:18-370,\n author = {Giannis Siglidis and Giannis Nikolentzos and Stratis Limnios and Christos Giatsidis and Konstantinos Skianis and Michalis Vazirgiannis},\n title = {GraKeL: A Graph Kernel Library in Python},\n journal = {Journal of Machine Learning Research},\n year = {2020},\n volume = {21},\n number = {54},\n pages = {1-5}\n}\n```\n\n## License\n\nGraKeL 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.\n\n## Acknowledgements\n\nWe would like to thank [@eddiebergman](https://github.com/eddiebergman) for modernizing our CI and extending our python support.\n",
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