gklearn.kernels.marginalizedKernel¶
@author: linlin
@references:
[1] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In Proceedings of the 20th International Conference on Machine Learning, Washington, DC, United States, 2003.
[2] Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert. Extensions of marginalized graph kernels. In Proceedings of the twenty-first international conference on Machine learning, page 70. ACM, 2004.
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marginalizedkernel(*args, node_label='atom', edge_label='bond_type', p_quit=0.5, n_iteration=20, remove_totters=False, n_jobs=None, verbose=True)[source]¶ Calculate marginalized graph kernels between graphs.
- Gn : List of NetworkX graph
- List of graphs between which the kernels are calculated.
- G1, G2 : NetworkX graphs
- Two graphs between which the kernel is calculated.
- node_label : string
- Node attribute used as symbolic label. The default node label is ‘atom’.
- edge_label : string
- Edge attribute used as symbolic label. The default edge label is ‘bond_type’.
- p_quit : integer
- The termination probability in the random walks generating step.
- n_iteration : integer
- Time of iterations to calculate R_inf.
- remove_totters : boolean
- Whether to remove totterings by method introduced in [2]. The default value is False.
- n_jobs : int
- Number of jobs for parallelization.
- Kmatrix : Numpy matrix
- Kernel matrix, each element of which is the marginalized kernel between 2 praphs.