zuliani99 / ManifoldLearning-GraphKernels

Analysis of Weisfeiler-Lehman Kernel using Isomap and Local Linear Embedded manifold learning technique among PPI and SHOCK datasets

Manifold Learning & Graph Kernels

Assignment

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

Choices

We decide to evaluate the Weisfeiler-Lehman Kernel among Isomap and Local Linear Embedding manifold learning technique.

The solution is provided by the usage of a Jupyter Notebook.