This repository contains Python scripts and notebooks for implementing various small scale diffusion networks for prototyping. These implementations can be used to experiment with different datasets to better understand the behavior and performance of diffusion networks in different scenarios.
- Clone the repository to your local machine.
- Ensure that you have the necessary Python libraries installed. You can check the
requirements.txtfile for a list of required libraries. - Browse through the available Python scripts and notebooks in the repository. Each implementation will have a brief description to help you understand its purpose.
- Choose a dataset to work with. You can use the provided sample datasets or import your own.
- Load the selected dataset into the script or notebook, and follow the instructions to run the specific implementation of the diffusion network.
- Experiment with different parameters, network structures, and datasets to observe how the diffusion process is affected. This will help you gain insights into the performance of various diffusion network implementations and their suitability for different use cases.
By running and experimenting with these Python scripts and notebooks, you'll be able to gain a deeper understanding of small scale diffusion networks and their potential applications.