This is a research project on grounded Laplacian eigenvalues based on discrete operators. We provide code and documentation for computing and analyzing eigenvalues and eigenvectors of discrete Laplacian operators on graphs, as well as applications related to graph embedding and machine learning.
Eigenvalues and eigenvectors of Laplacian matrices have wide applications in fields such as graph theory, machine learning, and data analysis. This project aims to provide a toolkit for computing and analyzing eigenvalues and eigenvectors of discrete Laplacian operators, along with example applications in graph embedding and machine learning.
Here, we provide code and algorithms for computing the eigenvalues and eigenvectors of discrete Laplacian operators on graphs. You can use these tools based on your data and requirements.
We also provide example applications, including graph embedding, community detection, and graph classification tasks. You can refer to these examples to understand how to use the computed eigenvalues and eigenvectors in real-world problems.
First, make sure you have the following dependencies installed in your environment:
- Python 3.x
- NumPy
- SciPy
- NetworkX
You can obtain the project code by cloning the repository:
git clone https://github.com/SamanthaWangdl/grounded_laplacian_eigenvalue.git
cd grounded_laplacian_eigenvalue