Network DMD Prediction

Dynamic Mode Decomposition for PredictingGraph Data

Introduction

Network DMD Prediction is a project that leverages the Dynamic Mode Decomposition with Control (DMDc) algorithm and Dynamic Mode Decomposition (DMD) to analyze and predict graph data. This project also utilizes node2vec, a node embedding technique, to transform graph structures into vector spaces. Subsequently, DMDc is applied to model and forecast the dynamic changes of the graph over time. The embedded vector is used to control input of DMDc.

Key Features

• DMD Implementation: Models dynamic systems to predict future states
• DMDc Implementation: Models dynamic systems with control inputs to predict future states.
• Node Embedding: Converts graph nodes into high-dimensional vectors using node2vec.
• Data Visualization: Visualizes model performance and prediction results for analysis.

Installation

Follow the steps below to set up the project in your local environment.

1. Clone the Repository

git clone https://github.com/Dexoculus/Network-DMD-Prediction.git
cd Network-DMD-Prediction

2. intall Required Packages

pip install -r requirements.txt

License

This project is provided under the MIT License.