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.
- marginalizedkernel(*args, node_label='atom', edge_label='bond_type', p_quit=0.5, n_iteration=20, remove_totters=False, n_jobs=None, chunksize=None, verbose=True)[source]
Compute marginalized graph kernels between graphs.
Parameters
- GnList of NetworkX graph
List of graphs between which the kernels are computed.
- G1, G2NetworkX graphs
Two graphs between which the kernel is computed.
- node_labelstring
Node attribute used as symbolic label. The default node label is ‘atom’.
- edge_labelstring
Edge attribute used as symbolic label. The default edge label is ‘bond_type’.
- p_quitinteger
The termination probability in the random walks generating step.
- n_iterationinteger
Time of iterations to compute R_inf.
- remove_tottersboolean
Whether to remove totterings by method introduced in [2]. The default value is False.
- n_jobsint
Number of jobs for parallelization.
Return
- KmatrixNumpy matrix
Kernel matrix, each element of which is the marginalized kernel between 2 praphs.