{"id":18877831,"url":"https://github.com/filipinascimento/bl-network-visualization","last_synced_at":"2026-02-19T06:30:18.886Z","repository":{"id":140080426,"uuid":"253880508","full_name":"filipinascimento/bl-network-visualization","owner":"filipinascimento","description":"This app generates simple 2D static visualizations for networks by using a force-directed algorithm. 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The current implementation uses the Large Graph Layout (LGL) algorithm.\n\n### Authors\n- [Filipi N. Silva](https://filipinascimento.github.io)\n\n### Contributors\n- [Franco Pestilli](https://liberalarts.utexas.edu/psychology/faculty/fp4834)\n\n\n### Funding Acknowledgement\nbrainlife.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.\n\n[![NSF-BCS-1734853](https://img.shields.io/badge/NSF_BCS-1734853-blue.svg)](https://nsf.gov/awardsearch/showAward?AWD_ID=1734853)\n[![NSF-BCS-1636893](https://img.shields.io/badge/NSF_BCS-1636893-blue.svg)](https://nsf.gov/awardsearch/showAward?AWD_ID=1636893)\n[![NSF-ACI-1916518](https://img.shields.io/badge/NSF_ACI-1916518-blue.svg)](https://nsf.gov/awardsearch/showAward?AWD_ID=1916518)\n[![NSF-IIS-1912270](https://img.shields.io/badge/NSF_IIS-1912270-blue.svg)](https://nsf.gov/awardsearch/showAward?AWD_ID=1912270)\n[![NIH-NIBIB-R01EB029272](https://img.shields.io/badge/NIH_NIBIB-R01EB029272-green.svg)](https://grantome.com/grant/NIH/R01-EB029272-01)\n\n\n### Citations\n1. 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)\n\n2. 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)\n\n## Running the App \n\n### On Brainlife.io\n\nYou 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.\n\n### Running Locally (on your machine)\nSingularity is required to run the package locally.\n\n1. git clone this repo.\n\n```bash\ngit clone \u003crepository URL\u003e\ncd \u003crepository PATH\u003e\n```\n\n2. Inside the cloned directory, edit `config-sample.json` with your data or use the provided data.\n\n3. Rename `config-sample.json` to `config.json` .\n\n```bash\nmv config-sample.json config.json\n```\n\n4. Launch the App by executing `main`\n\n```bash\n./main\n```\n\n### Sample Datasets\n\nA sample dataset is provided in folder `data` and `config-sample.json`\n\n## Output\n\nThe output folder contains the pdfs of the static visualizations for each network in input.\n\n#### Product.json\n\nThe `product.json` contains previews of the generated figures.\n\n### Dependencies\n\nThis 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:  \n\n```bash\n./main.py config.json\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffilipinascimento%2Fbl-network-visualization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffilipinascimento%2Fbl-network-visualization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffilipinascimento%2Fbl-network-visualization/lists"}