MRC Network Analysis: Explore Mathematical Research Collaborations 🌐📊

Welcome to the MRC-Network-Analysis repository! This project contains scripts designed for network analysis of topics identified by the Math Research Compass. By leveraging data from various sources, we aim to uncover insights into the landscape of mathematical research and its collaborations.

Table of Contents

• Project Overview
• Installation
• Usage
• Topics Covered
• Scripts
• Contributing
• License
• Contact
• Releases

Project Overview

The MRC-Network-Analysis project focuses on analyzing the connections and collaborations in mathematical research. It utilizes various tools and libraries to model and visualize research topics and their interrelations. By examining these networks, we can gain a better understanding of trends, influential papers, and key contributors in the field.

Installation

To get started with this project, you need to clone the repository and install the required dependencies. Follow these steps:

1. Clone the repository:

   git clone https://github.com/hevrk/MRC-Network-Analysis.git
   cd MRC-Network-Analysis

2. Install the necessary Python packages:

   pip install -r requirements.txt

Ensure you have Python 3.x installed on your system.

Usage

After setting up the environment, you can run the scripts to perform network analysis. The main script to start with is network_analysis.py. You can execute it using:

python network_analysis.py

This will generate visualizations and outputs based on the dataset provided.

Topics Covered

This project delves into various topics relevant to network analysis in mathematical research. Here are the main areas of focus:

• arxiv-dataset: Utilize data from arXiv to analyze research papers.
• bertopic: Implement topic modeling techniques to discover underlying themes.
• data-science: Apply data science methods to extract insights from the research network.
• mathematical-research: Focus on papers and collaborations in the mathematical domain.
• network-analysis: Explore the relationships and interactions within research networks.
• networkx: Use the NetworkX library for creating and manipulating complex networks.
• research-network: Study the connections between researchers and institutions.
• scientific-collaboration: Analyze patterns of collaboration among researchers.
• scientometrics: Measure and analyze scientific literature and its impact.
• topic-modeling: Discover topics within the research papers using various algorithms.

Scripts

The repository includes several scripts to facilitate network analysis:

• network_analysis.py: Main script for performing network analysis.
• data_preprocessing.py: Script for cleaning and preparing the dataset.
• visualization.py: Generate visualizations of the research network.
• topic_modeling.py: Implement topic modeling techniques on the dataset.

You can find more details about each script in their respective files.

Contributing

We welcome contributions to enhance this project. If you want to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push to your branch and create a pull request.

Please ensure your code adheres to the project's style guidelines and includes relevant tests.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For questions or feedback, please reach out to the repository owner:

• GitHub: hevrk

Releases

To download the latest release, visit the Releases section. Download and execute the relevant files to get started with the analysis.

You can also find the latest updates and versions there. Make sure to check back often for new features and improvements.

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Explore the intricacies of mathematical research through network analysis. We hope you find this project valuable in your own research endeavors!