Metadata-Version: 2.1
Name: grakel
Version: 0.1b7
Summary: A scikit-learn compatible library for graph kernels
Home-page: https://ysig.github.io/GraKeL/
Author: Ioannis Siglidis [LiX / DaSciM]
Author-email: y.siglidis@gmail.com
License: BSD
Project-URL: Documentation, https://ysig.github.io/GraKeL/
Project-URL: Send us Feedback!, http://www.lix.polytechnique.fr/dascim/contact/
Project-URL: Source, https://github.com/ysig/GraKeL/
Project-URL: Tracker, https://github.com/ysig/GraKeL/issues
Description: <p align="center">
          <img width="50%" src="https://raw.githubusercontent.com/ysig/GraKeL/0.1a7/doc/_figures/logo.svg?sanitize=true" />
        
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        **[Documentation](https://ysig.github.io/GraKeL/)** | **[Paper](https://arxiv.org/pdf/1806.02193.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 15 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)
        
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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/0.1a7/examples) in the `examples/` directory and our [tutorials](https://github.com/ysig/GraKeL/tree/0.1a7/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
        $ nosetests grakel
        ```
        
        ## Running examples
        
        ```
        $ cd examples
        $ python shortest_path.py
        ```
        
        ## Cite
        
        If you use GraKeL in a scientific publication, please cite our paper (https://arxiv.org/pdf/1806.02193.pdf):
        
        ```
        @article{siglidis2018grakel,
          title={GraKeL: A Graph Kernel Library in Python},
          author={Siglidis, Giannis and Nikolentzos, Giannis and Limnios, Stratis and Giatsidis, Christos and Skianis, Konstantinos and Vazirgiannis, Michalis},
          journal={arXiv preprint arXiv:1806.02193},
          year={2018}
        }
        ```
        
        ## 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.
        
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