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 this case I decided to use LocallyLinearEmbedding and Isomap, and to implement the Weisfeiler Lehman graph kernel.
A python 3 interpreter is required, along with the following modules:
pip install numpy
pip install matplotlib
pip install scikit-learn
pip install pandasAn IDE is required to run the main Jupyter Notebook.
git clone https://github.com/jgurakuqi/mitsuba-snapshot-tool.gitMIT License
Copyright (c) 2021 Elsa Sejdi
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