Read this article presenting a way to improve the disciminative power of graph kernels.
Choose one graph kernel among
- Shortest-path Kernel
- Graphlet Kernel
- Random Walk Kernel
- Weisfeiler-Lehman Kernel
- Choose one manifold learning technique among
Isomap Diffusion Maps Laplacian Eigenmaps Local Linear Embedding Compare the performance of an SVM trained on the given kernel, with or without the manifold learning step, on the following datasets:
- [PPI]
- [Shock]
The zip files contain csv files representing the adjacecy matrices of the graphs and of the lavels. the files graphxxx.csv contain the adjaccency matrices, one per file, while the file labels.csv contains all the labels
In order to run this project it's required a Python 3 installation, the modules libraries:
pip install numpy
pip install matplotlib
pip install imageio
pip install scikit-learnAlso download the required dataset as specified in the Requirements.
OPTIONALY Intel Scikit to largely improve performance on x86-compatible CPU or Intel GPU:
pip install scikit-learn-intelex In order to run the Jupyter Notebook a compatible IDE is required.
In this implementation I chose to implement the Shortest Path Kernel and use the scikit provided manifold techniques: SpectralEmbedding, LocallyLinearEmbedding. The provided project could be use to implement further kernels and manifold techniques, to compare their performance. Also, different datasets might be used, allowing to gain a better view about the behaviour of these techniques.
git clone https://github.com/jgurakuqi/graph-kernels-and-manifold-svmMIT License
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