Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/filipinascimento/bl-network-visualization
This app generates simple 2D static visualizations for networks by using a force-directed algorithm. The current implementation uses the Large Graph Layout (LGL) algorithm.
https://github.com/filipinascimento/bl-network-visualization
network
Last synced: 11 days ago
JSON representation
This app generates simple 2D static visualizations for networks by using a force-directed algorithm. The current implementation uses the Large Graph Layout (LGL) algorithm.
- Host: GitHub
- URL: https://github.com/filipinascimento/bl-network-visualization
- Owner: filipinascimento
- License: mit
- Created: 2020-04-07T18:34:52.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-04-13T19:36:20.000Z (over 3 years ago)
- Last Synced: 2023-10-20T19:49:54.179Z (about 1 year ago)
- Topics: network
- Language: Python
- Homepage:
- Size: 630 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
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.306)
# Network Visualization
This app generates simple visualizations for networks by using a force-directed algorithm. The current implementation uses the Large Graph Layout (LGL) algorithm.
### 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. Adai, Alex T., Shailesh V. Date, Shannon Wieland, and Edward M. Marcotte. "LGL: creating a map of protein function with an algorithm for visualizing very large biological networks." Journal of molecular biology 340, no. 1 (2004): 179-190. [https://doi.org/10.1016/j.jmb.2004.04.047](https://doi.org/10.1016/j.jmb.2004.04.047)
## Running the App
### On Brainlife.io
You can submit this App online at [https://doi.org/10.25663/brainlife.app.306](https://doi.org/10.25663/brainlife.app.306) 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`
## Output
The output folder contains the pdfs of the static visualizations for each network in input.
#### Product.json
The `product.json` contains previews of the generated figures.
### 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
```