Network Diffusion – Framework to Simulate Spreading Processes in Complex Networks

This repository contains code used to perform experiments and analyse results that have been attached to a paper published at Big Data Mining and Analytics.

The Network Diffusion package (a backbone of the expermients) is available on PyPI and GitHub.

Installation of environment

conda create --name bdma -y python=3.10
conda activate bdma
pip install -r requirements.txt
pip install network_diffusion==0.13.0
python -m ipykernel install --user --name bdma

Suspected-Infected-Removed + Unaware-Aware

Example of propagation two coexisting processes that influence each other: disease (following SIR model) and awarenes (following UA model). Perform experiments from sir_ua.ipynb. Details of model and results in diretory sir_ua.

Multilayer Independent Cascade Model initialised with Minimum Dominating Set

Example of propagation of Independent Cascade Model in multilayer networks with various seed selection methods. Especially a comparision of Minimum Dominating Set (a concept from domain of netork controlability) with centrality metrics.

Temporal Network Epistemology Model + CogSNet / Static Network

Comparison of a spreading of the Temporal Network Epistemology on two different network models built from the same temporal edge list. Preform experiments with tnem_cogsnet.ipynb. Results will be saved in tnem_cogsnet directory.

Temporal Linear Threshold Model + CogSNet / Static Network

Comparison of a spreading of the Temporal Linear Threshold Model on two different network models built from the same temporal edge list. Preform experiments with mltm_cogsnet.ipynb. Results will be saved in mltm_cogsnet directory.

Efficiency test

Comparison of time efficiency of models used in experiments. Execute them with efficiency_test.ipynb. Results will be saved in efficiency_test directory.

Citing

If you found the paper or the codebase useful in your research, please consider citing us as follows:

@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},
}

anty-filidor/bdma-experiments