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https://github.com/filipinascimento/bl-network-communities
App to obtain the community structure of networks by using the Louvain or Infomap methods. All the Louvain quality functions work for networks with negative weights.
https://github.com/filipinascimento/bl-network-communities
network
Last synced: about 2 months ago
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App to obtain the community structure of networks by using the Louvain or Infomap methods. All the Louvain quality functions work for networks with negative weights.
- Host: GitHub
- URL: https://github.com/filipinascimento/bl-network-communities
- Owner: filipinascimento
- License: mit
- Created: 2020-03-24T01:43:55.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-06-07T04:00:55.000Z (over 3 years ago)
- Last Synced: 2023-03-11T19:05:58.170Z (almost 2 years ago)
- Topics: network
- Language: Python
- Homepage:
- Size: 2.45 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://github.com/brain-life/abcd-spec)
[](https://doi.org/10.25663/brainlife.app.290)
# Network Communities
Obtain the community structure for networks using the Louvain or Infomap methods. Negative weights are supported only for Louvain.
### Authors
- [Filipi N. Silva](https://filipinascimento.github.io)
### Contributors
- [Franco Pestilli](https://liberalarts.utexas.edu/psychology/faculty/fp4834)
### Funding Acknowledgement
brainlife.io is publicly funded and for the sustainability of the project it is helpful to Acknowledge the use of the platform. We kindly ask that you acknowledge the funding below in your publications and code reusing this code.
[](https://nsf.gov/awardsearch/showAward?AWD_ID=1734853)
[](https://nsf.gov/awardsearch/showAward?AWD_ID=1636893)
[](https://nsf.gov/awardsearch/showAward?AWD_ID=1916518)
[](https://nsf.gov/awardsearch/showAward?AWD_ID=1912270)
[](https://grantome.com/grant/NIH/R01-EB029272-01)
### Citations
1. Avesani, P., McPherson, B., Hayashi, S. et al. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 6, 69 (2019). [https://doi.org/10.1038/s41597-019-0073-y](https://doi.org/10.1038/s41597-019-0073-y)
2. Blondel, Vincent D., Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. "Fast unfolding of communities in large networks." Journal of statistical mechanics: theory and experiment 2008, no. 10 (2008): P10008. [https://doi.org/10.1088/1742-5468/2008/10/P10008](https://doi.org/10.1088/1742-5468/2008/10/P10008)
3. Rosvall, Martin, and Carl T. Bergstrom. "Maps of random walks on complex networks reveal community structure." Proceedings of the National Academy of Sciences 105, no. 4 (2008): 1118-1123.[https://dx.doi.org/10.1073/pnas.0706851105](https://dx.doi.org/10.1073/pnas.0706851105)
4. Rubinov, Mikail, and Olaf Sporns. "Weight-conserving characterization of complex functional brain networks." Neuroimage 56, no. 4 (2011): 2068-2079. [https://doi.org/10.1016/j.neuroimage.2011.03.069](https://doi.org/10.1016/j.neuroimage.2011.03.069)
5. Tiago P. Peixoto. “Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models”, Phys. Rev. E 89, 012804 (2014). [https://doi.org/10.1103/PhysRevE.89.012804](https://doi.org/10.1103/PhysRevE.89.012804)
6. Tiago P. Peixoto. “Inferring the mesoscale structure of layered, edge-valued and time-varying networks”, Phys. Rev. E 92, 042807 (2015). [https://doi.org/10.1103/PhysRevE.92.042807](https://doi.org/10.1103/PhysRevE.92.042807)
## Running the App
### On Brainlife.io
You can submit this App online at [https://doi.org/10.25663/brainlife.app.290](https://doi.org/10.25663/brainlife.app.290) via the "Execute" tab.
### Running Locally (on your machine)
Singularity is required to run the package locally.
1. git clone this repo.
```bash
git clone
cd
```
2. Inside the cloned directory, edit `config-sample.json` with your data or use the provided data.
3. Rename `config-sample.json` to `config.json` .
```bash
mv config-sample.json config.json
```
4. Launch the App by executing `main`
```bash
./main
```
### Sample Datasets
A sample dataset is provided in folder `data` and `config-sample.json`. Extra examples are provided for other scenarios: `config-sample_negative.json`, `config-sample_layered.json` and `config-sample_nullmodel.json`.
## Output
The output is a `network` data with integrated community labels.
### Dependencies
This App only requires [singularity](https://www.sylabs.io/singularity/) to run. If you don't have singularity, you will need to install the python packages defined in `environment.yml`, then you can run the code directly from python using:
```bash
./main.py config.json
```