https://github.com/anty-filidor/bdma-experiments
Experimental repo for paper: https://doi.org/10.26599/BDMA.2024.9020010
https://github.com/anty-filidor/bdma-experiments
influence-maximization multilayer-networks network-control network-science social-learning temporal-networks
Last synced: about 2 months ago
JSON representation
Experimental repo for paper: https://doi.org/10.26599/BDMA.2024.9020010
- Host: GitHub
- URL: https://github.com/anty-filidor/bdma-experiments
- Owner: anty-filidor
- Created: 2023-09-18T13:08:17.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-02-21T16:06:35.000Z (over 2 years ago)
- Last Synced: 2025-01-01T19:44:38.972Z (over 1 year ago)
- Topics: influence-maximization, multilayer-networks, network-control, network-science, social-learning, temporal-networks
- Language: Jupyter Notebook
- Homepage:
- Size: 2.1 MB
- Stars: 1
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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](https://doi.org/10.26599/BDMA.2024.9020010).
The Network Diffusion package (a backbone of the expermients) is available on
[PyPI](https://pypi.org/project/network-diffusion/) and
[GitHub](https://github.com/anty-filidor/network_diffusion).
## Installation of environment
```bash
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:
```latex
@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},
}
```