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https://github.com/anty-filidor/network_diffusion
Python package for simulating spreading phenomena in complex networks
https://github.com/anty-filidor/network_diffusion
diffusion influence-maximization network-science networkx package python simulation spreading
Last synced: about 2 hours ago
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Python package for simulating spreading phenomena in complex networks
- Host: GitHub
- URL: https://github.com/anty-filidor/network_diffusion
- Owner: anty-filidor
- License: mit
- Created: 2019-08-04T12:01:53.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-11-06T13:32:02.000Z (10 days ago)
- Last Synced: 2024-11-06T14:32:55.885Z (10 days ago)
- Topics: diffusion, influence-maximization, network-science, networkx, package, python, simulation, spreading
- Language: Python
- Homepage: https://network-diffusion.readthedocs.io/en/latest/
- Size: 22 MB
- Stars: 16
- Watchers: 5
- Forks: 5
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Network Diffusion - Spreading Models in Networks
[](https://opensource.org/license/mit)
[](https://badge.fury.io/py/network-diffusion)
[](https://network-diffusion.readthedocs.io/en/latest)
[](https://codecov.io/gh/anty-filidor/network_diffusion)
[](https://app.fossa.com/projects/git%2Bgithub.com%2Fanty-filidor%2Fnetwork_diffusion?ref=badge_shield)
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.
## Short Example
```python
import network_diffusion as nd
# define the model with its internal parameters
spreading_model = nd.models.MICModel(
seeding_budget=[90, 10, 0], # 95% act suspected, 10% infected, 0% recovered
seed_selector=nd.seeding.RandomSeedSelector(), # pick infected act randomly
protocol="OR", # how to aggregate impulses from the network's layers
probability=0.5, # probability of infection
)
# get the graph - a medium for spreading
network = nd.mln.functions.get_toy_network_piotr()
# perform the simulation that lasts four epochs
simulator = nd.Simulator(model=spreading_model, network=network)
logs = simulator.perform_propagation(n_epochs=3)
# obtain detailed logs for each actor in the form of JSON
raw_logs_json = logs.get_detailed_logs()
# or obtain aggregated logs for each of the network's layer
aggregated_logs_json = logs.get_aggragated_logs()
# or just save a summary of the experiment with all the experiment's details
logs.report(visualisation=True, path="my_experiment")
```
## 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](https://www.researchgate.net/publication/348341904_Social_Networks_through_the_Prism_of_Cognition).
- **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**: 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.
- **PyTorch representation**: Network Diffusion offers a plausible converter of
the multilayer network to PyTorch sparse representation. That feature can
help in deep-learning experiments utilising complex networks (e.g. GNNs).
## Package Installation
To install the package, run this command: `pip install network_diffusion`.
Please note that we currently support Linux, MacOS, and Windows, but the
package is mostly tested and developed on Unix-based systems.
To contribute, please clone the repo, switch to a new feature branch, and
install the environment:
```bash
conda env create -f env/conda.yml
conda activate network-diffusion
pip install -e .
```
## Documentation
Reference guide is available here!
Please bear in mind that **this project is still in development**, so the API
usually differs between versions. Nonetheless, the code is documented well, so
we encourage users to explore the repository. Another way to familiarise
yourself with the operating principles of `network_diffusion` are projects
which utilise it:
- Generator of a dataset with actors' spreading potentials - _v0.16.0_ -
[repo](https://github.com/network-science-lab/infmax-simulator-icm-mln)
- Influence max. under LTM in multilayer networks - _v0.14.0 pre-release_ -
[repo](https://github.com/anty-filidor/rank-refined-seeding-bc-infmax-mlnets-ltm)
- Comparison of spreading in various temporal network models - _v0.13.0_ -
[repo](https://github.com/anty-filidor/bdma-experiments)
- Seed selection methods for ICM in multilayer networks - _v0.10.0_ -
[repo](https://github.com/damian4060/Independent_Cascade_Model)
- Modelling coexisting spreading phenomena - _v0.6_ -
[repo](https://github.com/anty-filidor/network_diffusion_examples)
## Citing the Library
If you used the package, please consider citing us:
```bibtex
@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={7},
number={3},
pages={637-654},
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 reference:
```bibtex
@inproceedings{czuba2022coexisting,
author={Czuba, Micha\l{} and Br\'{o}dka, Piotr},
booktitle={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},
}
```
## Reporting Bugs
Please report bugs on
[this](https://github.com/anty-filidor/network_diffusion/issues) board or by
sending a direct [e-mail](https://github.com/anty-filidor) to the main author.
## About Us
This library is developed and maintained by
[Network Science Lab](https://networks.pwr.edu.pl/) from Politechnika
Wrocławska / Wrocław University of Science and Technology / Technische
Universität Breslau and external partners. For more information and updates,
please visit our [website](https://networks.pwr.edu.pl/) or
[GitHub](https://github.com/network-science-lab) page.