network-diffusion 0.14.2

Network Diffusion is a package for simulating spreading phenomena.

Network Diffusion - spreading models in networks

This Python library provides a versatile toolkit for simulating diffusion processes in complex networks. It offers support for various types of models, including temporal models, multilayer models, and combinations of both.

Documentation is available here!

Key Features

• Complex Network Simulation: The library enables users to simulate diffusion processes in complex networks with ease. Whether you are studying information spread, disease propagation, or any other diffusion phenomena, this library has you covered.

• Temporal Models: You can work with temporal models, allowing you to capture the dynamics of processes over time. These temporal models can be created using regular time windows or leverage CogSnet.

• Multilayer Networks: The library supports multilayer networks, which are essential for modelling real-world systems with interconnected layers of complexity.

• Predefined Models: You have the option to use predefined diffusion models such as the Linear Threshold Model, Independent Cascade Model, and more. These models simplify the simulation process, allowing you to focus on your specific research questions.

• Custom Models: Additionally, Network Diffusion allows you to define your own diffusion models using open interfaces, providing flexibility for researchers to tailor simulations to their unique requirements.

• Centrality Measures: The library provides a wide range of centrality measures specifically designed for multilayer networks. These measures can be valuable for selecting influential seed nodes in diffusion processes.

• NetworkX Compatible: Last but not least, the package is built on top of NetworkX, ensuring seamless compatibility with this popular Python library for network analysis. You can easily integrate it into your existing NetworkX-based workflows.

How to install this package

To install package, run this command: pip install network_diffusion. Please note that we currently support Linux, MacOS, and Windows only.

To contribute, please clone the repo, switch to a new feature-branch, and install the environment:

conda env create -f requirements/environment.yml
conda activate network-diffusion
pip install -e .

Citing us

If you used the package, please cite us as:

@article{czuba2024networkdiffusion,
  title={Network Diffusion – Framework to Simulate Spreading Processes in Complex Networks},
  author={
    Czuba, Micha{\l} and Nurek, Mateusz and Serwata, Damian and Qi, Yu-Xuan and
    Jia, Mingshan and Musial, Katarzyna and Michalski, Rados{\l}aw and Br{\'o}dka, Piotr
  },
  journal={Big Data Mining And Analytics},
  volume={},
  number={},
  pages={1-13},
  year={2024},
  publisher={IEEE},
  doi = {10.26599/BDMA.2024.9020010},
  url={https://doi.org/10.26599/BDMA.2024.9020010},
}

Particularly if you used the functionality of simulating coexisting phenomena in complex networks, please add the following work:

@inproceedings{czuba2022coexisting,
    author={Czuba, Micha\l{} and Br\'{o}dka, Piotr},
    booktitle={2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)},
    title={Simulating Spreading of Multiple Interacting Processes in Complex Networks},
    volume={},
    number={},
    pages={1-10},
    year={2022},
    month={oct},
    publisher={IEEE},
    address={Shenzhen, China},
    doi={10.1109/DSAA54385.2022.10032425},
    url={https://ieeexplore.ieee.org/abstract/document/10032425},
}

Bugs reporting

Please report bugs on this board or by sending a direct e-mail to the main author.

About us

This library is developed and maintained by Network Science Lab at WUST and external partners. For more information and updates, please visit our website.