Friendship Recommendation Algorithm

Overview

This project focuses on developing a novel algorithm for friendship recommendation in social networks. The algorithm leverages the analysis of structural and profile-based mutual friends graphs to provide personalized friendship suggestions to users. The goal is to enhance the user experience and foster meaningful connections in the social network.

Methodology

Mutual Friends Graphs Analysis

• Structural Analysis: Our algorithm examines the structural properties of mutual friends graphs, including centrality measures, clustering coefficients, and community structures. This analysis helps identify potential friends who are well-connected within the user's social network.

• Profile Analysis: In addition to structural analysis, our algorithm considers the profile information of users and their mutual friends. This includes interests, hobbies, demographics, and other profile attributes. By analyzing profile data, we aim to recommend friends who share common interests and characteristics with the user.

Implementation

The project is implemented using MATLAB, a powerful programming environment for numerical computing and data analysis. MATLAB provides a robust platform for developing and testing the friendship recommendation algorithm.

Comparison with Other Methods

To assess the effectiveness of our algorithm, we have conducted simulations and comparisons with several other friendship recommendation methods, including:

• Fast Mo: A state-of-the-art recommendation method based on network motifs analysis.
• Fast Newman: An algorithm that leverages community detection techniques for friend recommendations.
• Only Community Detection: A method that focuses solely on community structures for recommending friends.
• Only Proposed FOF: Our novel algorithm, which combines structural and profile mutual friends graphs analysis for personalized recommendations.

We have designed comprehensive experiments to evaluate the performance and accuracy of each method, highlighting the unique advantages of our proposed algorithm.

Usage

To use this project:

1. Clone the repository to your local machine.
2. Open the MATLAB scripts provided.
3. Customize the input data, parameters, and configurations as needed.
4. Run the scripts to perform friendship recommendation simulations and analysis.

Contributors

• [Your Name]
• [Contributor 1]
• [Contributor 2]

License

This project is licensed under the [License Name] License - see the LICENSE.md file for details.

Acknowledgments

We extend our appreciation to [Mention Any Acknowledgments or References] for their support and contributions to the development of this innovative friendship recommendation algorithm.