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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]

Other implementations of graph kernels and graph benchmark datasets are available here.

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 and igraph libraries are needed.

The main function graphKernelMatrix is defined as:

void graphKernelMatrix(vector<igraph_t>& g, vector<double>& 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 <kernel_name> : An abbreviated kernel name (see the list below)
-p <parameter> : Parameter(s) in a kernel (if there are more than two, they should be comma-separated)
-i <input_file_list> : A file describing the list of graph file names
-g <input_file_path> : A path to the directory of graph files (the GraphML format is supported)
-o <output_file> : 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: mahito@nii.ac.jp

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