Network Alignment Project – Functions and Plot Generation

This repository contains supporting code and data for my thesis project on network alignment and its detectability phase transitions. The repository is structured into modular components to facilitate the reproducibility of the results presented in the thesis.

---

📁 Repository Structure

• functions.ipynb:
  This file contains all the core functions used throughout the project. The functions are organised into three categories:

  • Used Functions:
    These are the main functions used in the experimental pipeline (e.g., noise addition, MCMC sampling, etc.).

  • Plotting Functions:
    Utility functions used to generate all figures included in the thesis, including statistical visualisations, phase transition diagrams, and performance comparisons.

  • Auxiliary Functions:
    Helper utilities that assist the main procedures (e.g., energy distribution, plotting a set of graphs, etc.).

• executions.ipynb:
  This script/notebook calls the main and plotting functions from functions to generate the figures included in the thesis. Each code block typically corresponds to a specific figure or experimental result.

• results_*.csv:
  These files contain the raw results from various experimental runs. The naming convention follows the format: results_<noise_level>_<run_number>.csv

  For example, results_0.25_1.csv contains the first run with noise level f = 0.25.

---

📊 Usage and Reproducibility

To reproduce the plots and results from the thesis:

1. Ensure all required packages are installed (first cell on each .ipynb file).
2. Open and run the code in executions.ipynb.
3. Make sure that the corresponding .csv result files are present in the same directory.

Additionally, you can also modify the functions in functions.ipynb to experiment with new parameter values, different posterior distributions, or alternative sampling strategies (e.g., with or without parallel tempering).

---

🧩 Dependencies

This project was built using Python 3.10+ and uses the following libraries:

• numpy
• pandas
• seaborn
• matplotlib
• networkx
• scipy
• numba
• csv (standard)
• os (standard)
• copy (standard)
• math (standard)
• time (standard)
• random (standard)
• import_ipynb (for importing notebooks as modules in Jupyter)