An unified approach to link prediction in collaboration networks

This repository contains the code and associated files for the paper "Comparison of Traditional and Machine Learning Models for Link Prediction in Graphs". It explores and compares various approaches, from traditional statistical models to machine learning techniques, assessing their accuracy and efficiency in predicting links in complex networks.

Repository Structure

• Code: Implementation of the models and techniques used in the study, including statistical models such as ERGM and machine learning techniques like GCN and Word2Vec.
• Data: Network datasets used in the analysis, including Astro-Ph, Cond-Mat, Gr-Qc, Hep-Ph, and Hep-Th.
• Models: Files with the fitted models.

Requirements

To reproduce the experiments, ensure you have:

• Python 3.8 or higher
• Required libraries: networkx, scikit-learn, pandas, numpy, matplotlib, torch y tensorflow

Execution

1. Clone this repository:

   git clone https://github.com/damartinezsi/An-unified-approach-to-link-prediction-in-collaboration-networks.git
   cd An-unified-approach-to-link-prediction-in-collaboration-networks
   

2. Run the notebooks in the code directory to replicate experiments and view results. To skip model fitting, models can be loaded directly from the "models" folder.

Contact

For questions or comments about the code or the paper, feel free to reach out at damartinezsi@unal.edu.co