samanthawangdl / grounded_laplacian_eigenvalue

a algorithm for grounded laplacian eigenvalue optimization problem

Grounded Laplacian Eigenvalue

Project Introduction

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.

Table of Contents

• Background
• Eigenvalue Computation
• Applications
• How to Use
• Examples
• Contribution
• License

Background

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.

Eigenvalue Computation

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.

Applications

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.

How to Use

Environment Setup

First, make sure you have the following dependencies installed in your environment:

• Python 3.x
• NumPy
• SciPy
• NetworkX

Installation

You can obtain the project code by cloning the repository:

git clone https://github.com/SamanthaWangdl/grounded_laplacian_eigenvalue.git
cd grounded_laplacian_eigenvalue