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0.1.8
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  • Examples
    • Example of building a graph classification pipeline.
    • Graph classification on MUTAG using the shortest path kernel.
    • Graph classification on a dataset that contains node-attributed graphs.
    • Performing cross-validation n times, optimizing SVM’s and kernel’s hyperparameters.
    • Graph classification on MUTAG using the Weisfeiler-Lehman subtree kernel.
    • Graph classification on a randomly generated dataset of Erdos-Renyi graphs.
    • Retrieval of most similar document using the Weisfeiler-Lehman subtree kernel.
    • Example of transforming NetworkX graphs to GraKeL graphs.
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Examples¶

Examples that use graph kernels to perform various tasks (e.g., graph classification, retrieval of most similar document, etc).

Example of building a graph classification pipeline.

Example of building a graph classification pipeline.¶

Graph classification on MUTAG using the shortest path kernel.

Graph classification on MUTAG using the shortest path kernel.¶

Graph classification on a dataset that contains node-attributed graphs.

Graph classification on a dataset that contains node-attributed graphs.¶

Performing cross-validation n times, optimizing SVM's and kernel's hyperparameters.

Performing cross-validation n times, optimizing SVM’s and kernel’s hyperparameters.¶

Graph classification on MUTAG using the Weisfeiler-Lehman subtree kernel.

Graph classification on MUTAG using the Weisfeiler-Lehman subtree kernel.¶

Graph classification on a randomly generated dataset of Erdos-Renyi graphs.

Graph classification on a randomly generated dataset of Erdos-Renyi graphs.¶

Retrieval of most similar document using the Weisfeiler-Lehman subtree kernel.

Retrieval of most similar document using the Weisfeiler-Lehman subtree kernel.¶

Example of transforming NetworkX graphs to GraKeL graphs.

Example of transforming NetworkX graphs to GraKeL graphs.¶

Download all examples in Python source code: auto_examples_python.zip

Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

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