https://github.com/nlskrg/optimal_assignment_kernels

Source code for the paper "On Valid Optimal Assignment Kernels and Applications to Graph Classification", Nils M. Kriege, Pierre-Louis Giscard, Richard C. Wilson, NIPS 2016.
https://github.com/nlskrg/optimal_assignment_kernels

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Source code for the paper "On Valid Optimal Assignment Kernels and Applications to Graph Classification", Nils M. Kriege, Pierre-Louis Giscard, Richard C. Wilson, NIPS 2016.

• Host: GitHub
• URL: https://github.com/nlskrg/optimal_assignment_kernels
• Owner: nlskrg
• Created: 2017-11-15T15:30:48.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-03-08T15:54:26.000Z (about 7 years ago)
• Last Synced: 2024-10-30T15:50:45.881Z (7 months ago)
• Language: Java
• Size: 715 KB
• Stars: 10
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• awesome-graph-classification - [Java Reference

README

        
# Optimal Assignment Kernels

Source code for the paper [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), Nils M. Kriege, Pierre-Louis Giscard, Richard C. Wilson, NIPS 2016.

## Usage

The graph kernels contained in this package can be computed via a command line interface. Run the shell script `kkernel` to see a list of all available kernels and parameters.

### Example

The following command computes the Weisfeiler-Lehman optimal assignment kernel with 0 to 4 refinement steps for the data set ENZYMES:

```

./kkernel -d ENZYMES wloa -h 0,1,2,3,4

```

For each value of `h` the kernel matrix is computed and stored in the directory `gram` using the [LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) file format.

## Building from source

Run `ant` to build `kgraph.jar` from source. 

## Data sets

The repository contains the data set ENZYMES only. Further data sets are available from the website [Benchmark Data Sets for Graph Kernels](http://graphkernels.cs.tu-dortmund.de). Please note that in our experimental comparison the edge labels, if present, were ignored. In order to reproduce the published results, please delete the files `DS_edge_labels.txt`, where `DS` is the name of the data set.

## Terms and conditions

When using our code please cite:

	@InCollection{NIPS2016_6166,

	  title                    = {On Valid Optimal Assignment Kernels and Applications to Graph Classification},

	  author                   = {Kriege, Nils M. and Giscard, Pierre-Louis and Wilson, Richard},

	  booktitle                = {Advances in Neural Information Processing Systems 29},

	  publisher                = {Curran Associates, Inc.},

	  year                     = {2016},

	  editor                   = {D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett},

	  pages                    = {1623--1631}

	}

## Contact information

If you have any questions, please contact [Nils Kriege](https://ls11-www.cs.tu-dortmund.de/staff/kriege).

## Links

A Matlab implementation of the Weisfeiler-Lehman optimal assignment kernel is available from [Matlab File Exchange](http://de.mathworks.com/matlabcentral/fileexchange/64711-weisfeiler-lehman-optimal-assignment-kernel).