This project is a web-based platform designed to assist in the writing of academic reviews by helping users discover and manage connections between academic papers.
- Interactive Graph Visualisation: Visualise connections between academic papers dynamically using Cytoscape.js.
- User-Driven Connections: Add and remove connections based on user knowledge and insights.
- Automated Metadata Retrieval: Automatically download and integrate metadata from arXiv.
- Search Functionality: Quickly find papers and their connections using an efficient search feature.
- Python 3.x
- Django
- PostgreSQL
- Node.js
- npm
- Clone the repository:
git clone https://github.com/yourusername/academic-review-platform.git cd academic-review-platform - Back-end Setup:
- Navigate to the the back-end folder
cd review_backend - Create a virtual environment:
python -m venv env .\env\Scripts\Activate
- Install Python dependencies:
pip install -r requirements.txt
- Start the Django development server:
python manage.py runserver
- Front-end Setup:
- Start a new terminal and navigate to the front-end directory:
cd React\graph-visualization\
- Install npm dependencies:
npm install
- Start the React development server:
npm start
Once both the backend and frontend servers are running, you can access the platform by navigating to http://localhost:3000 in your web browser. Use the interface to explore academic paper connections, add or remove links, and search for specific papers.
Despite all of the subjects having papers downloaded, the database currently only has connections for Computer Science Machine Learning (500 papers) and Computer Science Artificial Intelligence (5000 papers).
To create more connections, navigate to the back-end directory and run the following command:
python manage.py assign_links "cs.AI"But check the arxiv_ids.py file for the appropriate subject ID.
This project was developed as part of an Integrated Master's thesis. I would like to thank my supervisor, Dr. D. Goodman, for his valuable feedback.