An open API service indexing awesome lists of open source software.

https://github.com/airscholar/alphateam

Complex Network Analysis Using Machine Learning
https://github.com/airscholar/alphateam

complex-networks deep-learning gnn graph-theory machine-learning

Last synced: 11 months ago
JSON representation

Complex Network Analysis Using Machine Learning

Awesome Lists containing this project

README

          

# Alpha Team App

This is the repository for the Alpha Team App.
The application is a web application that allows users to visualise and analyse complex networks.
The application is built using Flask.

# System Architecture
![System Architecture](docs/images/System_architecture.png)

# Scientific Paper

Read our scientific paper [here](Democratising_Complex_Network_Analysis.pdf).

## Running the application locally

1. Clone the repository
2. Navigate to the backend directory and run
- `export FLASK_APP=app.py` for Linux/Mac or `set FLASK_APP=app.py` for Windows
3. Run `flask run --host=0.0.0.0 --port=8000` to start the backend server
4. Open another terminal and navigate to the application directory
5. Run `flask run --host=0.0.0.0 --port={chosen port}` to start the frontend server
6. Open your browser and go to `http://127.0.0.1:{chosen port}`

# Docker Setup

Install Docker and Docker Compose on your machine. You can find the
instructions [here](https://docs.docker.com/install/).

## Running the application using Docker

1. Clone the repository
2. Run `docker build -t alphateamapp .` in the root directory of the project to build the Docker image
3. Run `docker run -d -p 8000:8000 -p 3000:3000 --name alphateam alphateamapp` to start the application
4. Open your browser and go to `http://127.0.0.1:3000`
5. Enjoy!
6. (Optional) Run `docker logs -f alphateam` to see the logs of the container

### Alternatively
You can run `docker pull airscholar/alphateamapp` to pull already built image and then run the container using
the command in step 3 above.

# Documentation

You can view the documentation in the `docs/` directory. The documentation is generated using Sphinx.

### To regenerate the documentation
In the root directory of the project, run `sh generate_docs.sh` to regenerate the documentation.