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https://github.com/BorgwardtLab/graph-kernels
Graph kernels
https://github.com/BorgwardtLab/graph-kernels
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Graph kernels
- Host: GitHub
- URL: https://github.com/BorgwardtLab/graph-kernels
- Owner: BorgwardtLab
- License: gpl-2.0
- Created: 2015-11-05T05:15:48.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2021-12-21T10:02:37.000Z (almost 3 years ago)
- Last Synced: 2024-07-16T18:11:54.972Z (5 months ago)
- Language: C++
- Size: 749 KB
- Stars: 56
- Watchers: 8
- Forks: 26
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Graph Kernels
A fast C++ implementation of graph kernels including:
* simple kernels between vertex and/or edge label histograms,
* random walk kernels (popular baselines), and
* the Weisfeiler-Lehman graph kernel (state-of-the-art).
Please see the following paper for more details:
* Sugiyama, M., Borgwardt, K. M.: **Halting in Random Walk Kernels**, *Advances in Neural Information Processing Systems (NIPS 2015)*, 2015 [[PDF]](https://papers.nips.cc/paper/5688-halting-in-random-walk-kernels.pdf)
Other implementations of graph kernels and graph benchmark datasets are available [here](https://www.bsse.ethz.ch/mlcb/research/machine-learning/graph-kernels.html).
## Usage
### In your program
You can compute the full kernel matrix by calling the function `graphKernelMatrix`.
To use it, you just need to include the header file "graphKernels.h" in your program.
The [Eigen](http://eigen.tuxfamily.org) and [igraph](http://igraph.org/c/) libraries are needed.
The main function `graphKernelMatrix` is defined as:
```
void graphKernelMatrix(vector& g, vector& par,
string& kernel_name, MatrixXd& K);
```
* `g`: a vector of input graphs
* `par`: a vector of parameters
* `kernel_name`: a string to specify a kernel (see the list below)
* `K`: the full kernel matrix will be returned here
### In terminal
To try the code, we also provide a graph benchmark dataset "mutag" and a test code "graphKernels_test.cc", which includes input and output interface for graph files.
For example:
```
$ cd src/cc
$ make
$ ./gkernel -k kR -p 1,2,1 -i ../../data/mutag.list -g ../../data/mutag/ -o mutag_kR.kernel
> Reading files ... end
> Information:
Kernel: k-step random walk kernel
Parameter: k = 3, lambda = (1, 2, 1)
Number of graphs: 188
The average number of vertices: 17.9309
The maximum number of vertices: 28
The average number of edges: 19.7926
The maximum number of edges: 33
> Computing the kernel matrix ... end
Runtime for the kernel matrix computation: 2.9501 (sec.)
> Writing the kernel matrix to "mutag_kR.kernel" ... end
$ ./gkernel -k WL -p 5 -i ../../data/mutag.list -g ../../data/mutag/ -o mutag_WL.kernel
> Reading files ... end
> Information:
Kernel: Weisfeiler-Lehman kernel
Parameter: h = 5
Number of graphs: 188
The average number of vertices: 17.9309
The maximum number of vertices: 28
The average number of edges: 19.7926
The maximum number of edges: 33
> Computing the kernel matrix ... end
Runtime for the kernel matrix computation: 0.00567007 (sec.)
> Writing the kernel matrix to "mutag_WL.kernel" ... end
```
To compile the program, please edit paths in the "Makefile" according to the location of Eigen and igraph libraries in your environment.
#### Command-line arguments
`-k ` : An abbreviated kernel name (see the list below)
`-p ` : Parameter(s) in a kernel (if there are more than two, they should be comma-separated)
`-i ` : A file describing the list of graph file names
`-g ` : A path to the directory of graph files (the GraphML format is supported)
`-o ` : An output file of the full kernel matrix
## List of graph kernels
The following graph kernels are supported:
The second column (Abbrev.) is used for the third argument of `graphKernelMatrix`.
Kernels | Abbrev. | Parameter
-------------------------------------------------- | ------- | ---------
Linear kernel between vertex histograms | V | None
Linear kernel between edge histograms | E | None
Linear kernel between vertex-edge histograms | VE | None
Linear kernel combination (V + λVE) | H | λ
Gaussian RBF kernel between vertex histograms | VG | σ
Gaussian RBF kernel between edge histograms | EG | σ
Gaussian RBF kernel between vertex-edge histograms | VEG | σ
Geometric random walk kernel | GR | λ
Exponential random walk kernel | ER | β
k-step random walk kernel | kR | λ0, λ1, ..., λk
Weisfeiler-Lehman subtree kernel | WL | h
## Acknowledgements
The development of the graphkernels open access software package was financially supported by the Horizon 2020/CDS-QUAMRI/634541 project.
This support is gratefully acknowledged.
## Contact
Author: Mahito Sugiyama
Affiliation: National Institute of Informatics, Japan
E-mail: [email protected]